The impact of lifestyle on environmental factors: An empirical analysis of French citizens’ behaviour towards environment

Abstract

Several studies have been developed to show the importance of household carbon emissions and further investigate their sources and driving factors. These studies have paid attention to the impact of household behaviour with regard to energy use and introduced the concept of lifestyle into the study of personal carbon emissions including income, household size, and household structure. Despite the abundance of studies in the literature, it is quite surprising to note the scarcity of studies in France that address the impact of lifestyle on the environment and climate change. We use a nationwide dataset that provides a high-quality nationwide comprehensive survey of French lifestyle and their behaviour towards the environment. The results of this paper highlight the important and significant impact of the lifestyle of French citizens on transport, energy use, consumption, food waste, and clothing. Moreover, they allow assessing the impact of lifestyle on environmental factors in order to manage the harmful effect of climate change on French citizens. Therefore, this study provides decision-makers with extensive valuable information for promoting environmentally sustainable lifestyles and consumption.

Keywords

lifestyle environmental factors French citizens carbon emissions VAR model

Introduction

The average global temperature was 1.3°C higher in 2016 than in 1880 (Li et al., 2019). The Intergovernmental Panel on Climate Change (IPCC, 2021) projects that the average global surface temperature will rise by between 1 and 5.7°C (1.8–10.3 F) by the end of the century (Auffhammer, 2022). According to the IPCC (2021), this shift in the mean of the global surface temperature distribution will be accompanied by significant increases in the frequency and intensity of extreme heat events (Auffhammer, 2022). Climate experts predict that global temperature will go up 1–6.4°C in this century, and sea levels will rise 16.5–53.8 cm (Li et al., 2019). According to the World Bank (2007), Carbon dioxide (CO₂) is the main greenhouse gas that leads to global warming and climate change. In particular, CO₂ emissions are influenced by households and personal lifestyles that contribute significantly to the total value (Li et al., 2019; Wei et al., 2007). In developed countries, such as the United States of America and the United Kingdom, the share of household emissions can exceed 70% of the total (Baiocchi et al., 2010; Bin and Dowlatabadi, 2005). In other countries, improvements in living standards have increased consumer demand and thereby caused rapid expansion in energy requirements, which creates a new source of growth in carbon emissions (Su and Ang, 2017).

In France, household carbon emissions can result from the direct use of fossil fuels and electricity, or indirectly from consuming final goods and services. Carbon emissions generated by production are essentially driven by final consumer demand (Wei et al., 2007). Schipper et al. (1989) indicated that about 45 or 55% of total energy use is influenced by consumers’ activities for personal transportation, personal services, and homes. In this sense, production and emissions associated with it are linked to citizens’ behaviour towards the environment. Duchin (1998) argued that most environmental degradation can be traced to the behaviour of consumers either directly, through activities like the disposal of garbage or the use of cars, or indirectly through the production activities undertaken to satisfy them (Rees, 1995). Therefore, the impact of citizens’ lifestyles on energy consumption can have significant ecological consequences.

Recent studies have shown increasing interest in this issue and investigated carbon emissions in many countries (Bohringer and Rosendahl, 2022; Wei et al., 2007; Xu et al., 2016; Şimşekoğlu et al., 2015). These studies also pay attention to the impact of citizens’ behaviour with regard to energy use and introduced the concept of lifestyle into the study of personal carbon emissions including income, household size, and household structure (Li et al., 2019). In addition to these studies, Pojani and Stead (2018) have shown that social mechanisms and processes, such as status seeking (the automobile as a status symbol), freedom seeking, or lack of trust in others’ cooperativeness perpetuate urban transport problems. Moreover, the two authors explained that the publicity generated by the car industry is well ahead of sustainable urban transport promotion. Some of these studies were trying to determine the impact of personal lifestyle on environmental change. Thanks to a survey in Beijing (826 respondents), Yang et al. (2016) showed that eco-friendly communities have significantly lower residential carbon emissions in Beijing. Qu et al. (2013) found that it is difficult to reduce household carbon emissions in northwestern arid alpine regions in China. Despite the abundance of studies in the literature, it is quite surprising to note the scarcity of studies in France that address the impact of lifestyle on the environment. The central questions affecting many municipalities in France deal with economic, social, and environmental sustainability. Therefore, the aim of this paper is to show how environmental concerns influence citizens’ sustainable behaviour (Bøhlerengen and Wiium, 2022; Gjerstad and Fløttum, 2021), and to investigate how policymakers use a range of environmental factors to design and roll out appropriate environmental policies.

Based on these arguments, this paper contributes to the literature by investigating the impact of lifestyle on environmental factors through an empirical analysis of French citizens’ behaviour towards the environment. It also allows us to address the question of environmental management in France by addressing the issues of public transportation, energy, and waste (Ashmore et al., 2017; Fazey et al., 2006; Raymond et al., 2010). We use a nationwide dataset that provides a high-quality nationwide comprehensive survey of French lifestyle and their behaviour towards the environment. The remainder of this paper is organized as follows: We start by reviewing the relevant literature on lifestyle and household carbon emissions. After reviewing the relevant literature, we introduce the data, the method, environmental factors, and other variables and then illustrates the model used in this paper. Then we report and discuss the main results obtained, analyzing the French environmental management that helps to identify degradation factors and implement strategies to mitigate them (Raymond et al., 2010). Finally, we conclude by providing a clear response to the hypotheses formulated and presents some limitations that need to be addressed with further research that are directly linked to our findings.

Literature review

Carbon emissions at the household level have been a hot topic in recent years (Li et al., 2019). Several studies have been developed recently to show the importance of household emissions and further investigate their sources and driving factors (Biesiot and Noorman, 1999; Bohringer and Rosendahl, 2022; Gjerstad and Fløttum, 2021; Kaida and Kaida, 2016; Wei et al., 2007). While some of them focus on the relationship between household energy consumption and direct carbon emissions (Biesiot and Noorman, 1999), others consider consumption patterns that have significant impacts on both direct and indirect household carbon emissions (Bin and Dowlatabadi, 2005; Duchin, 1998; Menzel, 2013; Saari et al., 2021; Schipper et al., 1989; Su et al., 2017; Xu et al., 2016). According to Druckman and Jackson (2009), factors shown to affect household carbon emissions include household income (Büchs and Schnepf, 2013), the size of households (Underwood and Zahran, 2015), energy supplies (Kerkhof et al., 2009), education (Andersson et al., 2014), and other individual, household, and environmental characteristics (Holian and Kahn, 2015).

Lifestyle, household carbon emissions, and climate change

Behavioural economics suggests that subjective factors, such as beliefs and preferences, can affect individual decision-making (Wei et al., 2007). Because of the looming threat of climate change and environmental problems in the World, reinforced by an increasing amount of public outreach, people now have more information and knowledge than before about carbon emissions (Li et al., 2019; Saari et al., 2021; Wei et al., 2007). In the literature, only a few studies have investigated the impact of lifestyle on environmental factors by analyzing the relationship between awareness and household carbon emissions. Without connecting lifestyle to carbon emissions, Gjerstad and Fløttum (2021) have investigated the linguistic and narrative structures of answers by Norwegian citizens in a public survey dealing with climate change lifestyle. Using a sample of 1920 Canadian respondents, Wilson et al. (2013) find that carbon emissions and subjective well-being are not closely related. In a Swedish survey of 1000 respondents, Andersson et al. (2014) obtained results almost similar to those obtained by Wilson et al. (2013). By exploring the link between low-carbon awareness and behaviour among residents in Tianjin (China), Bai and Liu (2013) found that the level of behaviour is higher than the level of awareness, indicating a gap between low-carbon awareness and behaviour. Various kinds of measures for emission reduction of greenhouse gases have also been studied in relation to individuals’ behaviour and lifestyle. One of these studies has focused on ‘carbon capability’, referring to meanings associated with carbon and individuals’ abilities and motivations to reduce emissions (Whitmarsh et al., 2011).

In line with the existing literature, Gjerstad and Fløttum (2021) indicate that a significant body of research is available on how climate change is represented and perceived (Pearce et al., 2015). However, a paucity of knowledge about the impact of French citizens’ lifestyles on environmental factors means that there is a need for research on this. In fact, climate change concerns many aspects of our lives and affects how we think about different important questions, from our personal lifestyle choices as consumers, and our political behaviour as citizens, to how we perceive the fate of our planet and the future of humanity (Gjerstad and Fløttum, 2021). Therefore, awareness of environmental problems might inspire people to change their lifestyle (Gadenne et al., 2011) and then affect consumption behaviour (Carter, 2011). Although useful for understanding the issues of global warming, these different studies are developed across economic approaches which do not provide a managerial response to environmental problems (Geels, 2005; Gorges and Holz-Rau, 2021; Olsson and Folke, 2001; Raymond et al., 2010; Reid et al., 2006). There have been significant developments in the way environmental considerations have been implemented, however, managing environmental factors within Ecology, Aliment, Clothing, Energy, Waste, and Transportation contexts in France remains a challenge.

Climate change and lifestyle in the French context

Following Tvinnereim et al. (2017)’s definition, lifestyle will be here understood as a way of living that reflects the behaviour, interests, and values of individuals, expressed in both work and leisure activities. Lifestyle issues that are often seen as relevant for individuals are related to transport, house building, energy use, consumption, food waste, and holidays (Gjerstad and Fløttum, 2021). This definition provides (1) relevant evidence to substantiate the research question in the French context and (2) it is likely to generate rich information about the issues under study: ecological dimension, food (aliment and nutrition), clothes, energy, waste, and transportation. A study on the situation in France can usefully help to understand the behaviour of citizens towards climate change and also understand ‘intentions to change personal activities to reduce carbon dioxide emissions’ such as travel, energy use at home, food consumption, and involvement in environmental organizations (Sundblad et al., 2014: 13). Using two surveys carried out in the Netherlands and the USA, De Boer et al. (2016) showed that a transition to a low-carbon society can significantly benefit from a special focus on the food-related options to involve more consumers and to improve mitigation. Bin and Dowlatabadi (2005) illustrated the relationships among different lifestyles on energy consumption and the related CO₂ emission. These two authors indicate that people may use energy directly whilst, on the other hand, there is a need to buy and use a range of products in order to meet their basic needs for commodities, such as clothing, food, housing, and travelling. This reality can be transposed to the French context which has not been sufficiently investigated in terms of the relationship between lifestyle and environmental protection. However, there are several interesting and relevant studies focussing on various single factors related to lifestyle in many countries (Gjerstad and Fløttum, 2021). We could also mention Brobakk’s research (2017) presenting a survey on Norwegian farmers’ behaviour towards emissions reduction. Investigating how psychological factors determine self-reported energy-efficient behaviour, Von Borgstede et al. (2013) concluded that there was an increased awareness among the public of the need for lifestyle changes, which could facilitate the implementation of policies promoting environmental behaviour (Poortinga et al., 2004; Von Borgstede et al., 2013; Gjerstad and Fløttum, 2021). In a qualitative study through 30 interviews with householders in a city in the UK, Paddock (2017) concludes that food practice provides a nexus point around which change can be more effectively conceptualised for public policies aimed at inculcating more sustainable ways of life (Paddock, 2017).

Based on these discussions, it is clear that existing studies have mainly focused on these lifestyle issues separately, rarely on the combination of all these issues (transport, energy use, consumption, food waste, etc.). As the world actively seeks ways to cope with the effects of global warming, there is a vital need for a reliable environmental management to support the efforts pursued by governments to address the problems of climate change (Fazey et al., 2006; Geels, 2005; Raymond et al., 2010; Reid et al., 2006). Existing studies have been carried out in several countries by addressing several issues. Despite the abundance of studies in the literature, it is quite surprising to note the scarcity of studies in France that address the impact of lifestyle on the environment and climate change. Although several studies have brought to an understanding that lifestyle can have a positive impact on the environment (Ashmore et al., 2017; Bohringer and Rosendahl, 2022; Li et al., 2019; Xu et al., 2016; Yang et al., 2016; Şimşekoğlu et al., 2015), they do not, however, enable to identify and understand the factors or elements likely to explain how policymakers manage environmental issues through citizens’ lifestyles (transport, clothing, waste, etc.) and their impact on climate change. This paper aims to fill in the gap by studying the impact of French citizens' lifestyle on many environmental factors through a managerial perspective that consider environmental management as a multi-faceted and multidisciplinary practice that aims at putting in place policies and strategies to improve citizens’ well-being and to reduce negative impacts on the environment by promoting responsible behaviours (Saari et al., 2021) and more particularly sustainable transport behaviours (Gorges and Holz-Rau, 2021). Because one of the core challenges for French government is to ensure that transport activity continues to support overall societal objectives, we will also focus on transportation policy to better understand the French mobility management which is symbolized by the meeting people’s travel needs while reducing the transport sector’s carbon footprint (Ashmore et al., 2017; Pojani and Stead, 2017).

Data and methodology

Data sources

The survey data used in this study is from the National Foundation of Political Sciences, conducted by Ivalo Petev and Yoann Demoli from the Lifestyles and Environment Survey (SVEN) in France (Table 1). The dataset (Table 1), which provides a high-quality nationwide comprehensive survey of French lifestyle and their behaviour towards the environment, was produced by the Sociopolitical Data Center (CDSP-Sciences Po/CNRS) as part of the Longitudinal study by Internet for the social sciences (ELIPSS) panel: an Internet panel representative of the population aged 18 to 79 residing in metropolitan France. The sample consists of 2362 individuals.

Table 1.

Descriptive statistics.

Subsystem	ATT	Ecology	Aliment	Clothes	Energy	Waste	Transport
	Att_nap	Att_poll	Al_mot	Hab_vet	Eng_el	Dech_re	Tran_dt
Component	Att_prob	Att_eco1	Al_ach	Hab_ch	Eng_gaz	Dech_re_disp	Tran_dta
	Att_sol	Att_eco2	Al_acq		Eng_eqb	Dech_rec_pr	Tran_dttc
		Att_eco3	Al_jet		Eng_eqc	Dech_sac	Tran_dvm
		Att_org_eco	Al_mang		Eng_eqd
					Eng_app
					Eng_appbis
					Eng_gest
					Eng_cahauf
					Eng_temp

The randomly selected panellists respond to surveys every month using a tablet and a mobile Internet subscription made available to them. Table 1 presents the 33 variables used to construct the 7 subsystems of the principal component analysis implemented in this study¹.

Attitude towards the environment (ATT)

Three variables capture the attitude of the selected French citizens towards the environment: (1) the relationship between humans & environment (ATT_NAP), (2) Environmental challenges (ATT_PROB), and (3) Solutions (ATT_SOL).

Ecology (ECO)

The representation of the ecological dimension is captured by five variables. The variable ATT_POLL is capturing if the citizen is considering himself/herself as a polluting person. The variable ATT_Eco1 captures if the citizen often talks about the environment with family members. The variable ATT_Eco2 captures if the citizen often talks about the environment with friends. The variable ATT_Eco3 captures if the citizen talks about the environment with colleagues. The variable ATT_OrgEco captures if the citizen is a member of any association other than sports ones.

Aliment and Nutrition (AL)

The section aliments contain five variables: (1) The variable AL_MOT captures if the following criteria: Price, health benefit, brand, taste, geographical origin, and biological label are taken into account while buying products. (2) The variable AL_ACH captures how often in the household the alimentary products are bought in the supermarket, bakery, local producers, market, and internet. (3) The variable AL_ACQ represents if the citizen has access to free products among the following: vegetable garden, self-made jam, fishing and picking, and other products. (4) The variable AL_JET is capturing if some products were thrown last week and what was the reason. (5) The variable AL_MANG captures the type of weekly consumed products among frozen products, cooked food, sodas, juices, fruits, seafood, beef, and canned products.

Clothes (HAB)

This section has two variables: The variable HAB_VET captures the type of clothes or shoes bought during the last 12 months and the sensitive criteria while choosing clothes and shoes. The variable HAB_CH indicates the total number of shoes bought during the last 12 months.

Energy (ENG)

In this section energy is represented by 10 variables:

- The variable ENG_EL captures the information contained in the electricity bills of the selected household such as the amount (Euro), the quantity of electricity consumption (KWH), and the month.

- The variable ENG_GAZ refers to the information contained in the natural gas bills of the selected household.

- The variable ENG_EQB captures the information related to the type of energy used for the heater (electricity, natural gas, coal, trees, urban heating, other sources, or not concerned).

- The variable ENG_EQC reviews the type of energy to heat water in the household between electricity, natural gas, coal, and others.

- The variable ENG_EQD gives information about the energy used for cooking in the main house between electricity, natural gas/coal, or other sources.

- The variable ENG_APP captures the number of home appliances in the house if the household got new home appliances in the last 5 years and their usefulness.

- The variable ENG_APPBIS catches which ones of oven, microwave, washing machine, television, and laptop the selected French citizens have.

- The variable ENG_GEST gives information about the frequency in which they accomplish the following actions: turning off the light while leaving any place in the house, using the washing machine only when there are plenty of clothes, washing clothes at low temperatures, not leaving the television or laptop in sleep mode, and dry clothes with air.

- The variable ENG_CHAUF captures the conditions in which the heater is turned off in winter in the household.

- The variable ENG_TEMP represents in which condition in winter the French household reduces the heater.

Waste (DECH)

The section waste is captured by four variables:

- The variable DECH_RE is capturing if the French citizens are sorting the following wastes or not: glass bottles, aluminium, iron or steel boxes, plastic bottles, paper/boxes, and alimentary trash.

- The variable DECH_RE_DISP stands for the type of recyclable waste making part of a selective pick up in the town, or they are not concerned.

- The variable DECH_REC_PR seizes if French sort the following waste: glass bottles, aluminium, iron or steel boxes, plastic bottles, paper/boxes, and alimentary trash.

- The variable DECH_SAC takes into account the quantity of non-sorted waste that the household throws per week based on the number and the size of the bag.

Transportation (TRAN)

The section transportation is made of four variables: (1) The variable TRAN_DT captures the type of transportation the selected French citizens are using to go to their work. (2) The variable TRAN_DTA focuses on the type of transportation used by the selected French citizens to accompany kids or family members to school or the workplace. (3) The variable TRAN_DTTC captures the reasons why French citizens do not usually use public transportation to go to work or accompany their family members to school or the workplace. (4) The variable TRAN_DVM captures the transportation mode used to go to a far place from home for at least one night during the last months.

Theoretical background

The use of an indicator is necessary to capture an observable variable used to describe a hardly sizeable phenomenon. However, a multidimensional economic, societal, or environmental phenomenon can be observed only through a synthetic index, able to aggregate different basic indicators. The aggregation of a multidimensional reality within one single number seems to be a tough exercise with a lot of constraints, but numerous such attempts exist in the literature. We can therefore see various techniques that can be used to create environment subsystem indexes. The first method aggregate index is built as a weighted average of distinct indicators (Rouabah, 2007). This method is used currently by the National Bank of Turkey. The normalization and aggregation procedures have been also used to calculate the banking stability aggregate index for the Czech banking sector (Gersl and Hermanek, 2006). A second method consists of the construction of an aggregate index based on daily data. This method has been experienced by Nelson and Perli (2007) in the case of the US financial system, where they proved that aggregating the fragility index can contribute to the forecasting of the probability regarding the occurrence of a turbulent period. The last method is that enabling a comparison between the individual economic, environmental, or societal indicators characterizing different subsystems and it consists of a hierarchy of individual indicator values (the principal component analysis ‘the aggregate index components’). This last method is the one retained for our study. No matter its main limit with regard to the minimum differences between the values of the indicators with the same weight within the aggregate index, the principal component analysis (PCA) is suitable for our study as it is pertinent to analyse a large number of variables subject to internal correlation and transform them into an uncorrelated small number of principal components (Jolliffe, 2002).

Research technique

Due to the large sample of our data, the PCA is used first to create the following subsystems: environmental issues (Att), ecological dimension (Eco), aliments or products (Al), clothing (Hab), energy consumption (Eng), waste (Dech), and the transportation (Tran), then a second PCA is employed to create representative index of each subsystem and the rule is to retain only component having eigenvalue above 1 for the construction of each index. Once the aggregate final indexes of French citizens’ consumption habits and environmental concerns have been created, then we investigate the relationship between French citizens' consumption habits and the environmental concerns using the VAR model, and finally, we estimate the OLS method to assess the impact of French consumption habits on the environment, this is to verify the effectiveness of the causality found in the VAR model.

Methodology

The principal component analysis

The principal component analysis (PCA) is a technique commonly used to analyse a large number of variables subject to internal correlation and transform them into an uncorrelated small number of principal components (Jolliffe and Cadima, 2016), and the rule is to retain only component having eigenvalue above 1. This research employs the PCA method to construct 7 indexes representative of the 7 following subsystems: the attitude of French hold towards environmental issues (Att), the representation of the environment through the ecological dimension (Eco), the type of aliments or products consumed by French people (Al), the type of clothes worn by French people (Hab), the quantity of energy consumption by French people (Eng), the management of waste in the household (Dech), and the transportation usually used by French people (Tran). These results of the PCA are used as inputs for the Vector Autoregressive (VAR) model and the OLS estimator.

Vector autoregressive model

The VAR model is similar to any simultaneous equation modelling, the only difference is that in a VAR model, all variables are considered endogenous, and there are no exogenous variables. This paper uses specifically, the Impulse response functions to analyse the relationship between French consumption behaviour and environmental related questions through the following indexes: Att, Eco, Al, Hab, Eng, Dech, and Tran.

Ordinary least squares method

To assess the impact of French citizens’ lifestyles on the environment, we use the Ordinary Least Squares estimation. This technique is useful and is widely used to estimate the parameters of a linear regression model. The OLS method minimizes the sum of the squared errors, that is, the difference between observed values and predicted values as it assumes homogeneity of intercepts and slopes giving equal weight to the within and between variances in the dataset.

Models specifications

PCA

A T T = α_{1} A t t_{n a p} + α_{2} A t t_{p r o b} + α_{3} {A t t}_{s o l}

(1)

The dependent variable ATT represents the attitude towards environmental questions index, and the independent variables stand respectively for the relationship between humans and the environment ( $A t t_{n a p}$ ), the attitude towards environmental problems ( $A t t_{p r o b}$ ), and the attitude towards environmental solutions ( $A t t_{s o l}$ ).

E C O = β_{1} A t t_{p o l l} + β_{2} A t t_{e c o 1} + β_{3} {A t t}_{e c o 2} + β_{4} {A t t}_{e c o 3} + β_{5} {A t t}_{o r g e c o}

(2)

The dependent variable ECO characterizes the environmental or ecological index, and the independent variables represent respectively, the attitude as a polluter (

A t t_{p o l l}

), talking about the environment with family members (

A t t_{e c o 1}

), talking about the environment with friends (

{A t t}_{e c o 2}

), Talking about the environment with colleagues (

{A t t}_{e c o 3}

), making part of an organization other than sports ones (

{A t t}_{o r g e c o}

)

A L = δ_{1} {A l}_{m o t} + δ_{2} {A l}_{a c h} + δ_{3} {A l}_{a c q} + δ_{4} {A l}_{j e t} + δ_{5} {A l}_{m a n g}

(3)

The dependent variable AL captures the type of food consumption index, and the following independent variables stand respectively for, the criteria used to buy a type of product ( ${A l}_{m o t})$ , how often alimentary products are bought in different places $({A l}_{a c h}$ ), the type of products got for free ( ${A l}_{a c q})$ , the alimentary product thrown ( ${A l}_{j e t})$ , type of aliments or products eaten ( ${A l}_{m a n g})$

H A B = θ_{1} {H a b}_{v e t} + θ_{2} {H a b}_{c h}

(4)

The dependent variable HAB describes the type of clothing index, and the independent variables represent, respectively, the type of shoes and clothes bought in the last 12 months, and the criteria for shopping $({H a b}_{v e t})$ indicates the total number of shoes bought during the last 12 months $({H a b}_{c h})$ .

E N G = ρ_{1} {E n g}_{e l} + ρ_{2} {E n g}_{g a z} + ρ_{3} {E n g}_{e q b} + ρ_{4} {E n g}_{e q c} + ρ_{5} {E n g}_{e q d} + ρ_{6} {E n g}_{a p p} + ρ_{7} {E n g}_{a p p b i s} + ρ_{8} {E n g}_{g e s t} + ρ_{9} {E n g}_{c h a u f} + ρ_{10} {E n g}_{t e m p}

(5)

The dependent variable ENG indicates the energy consumption index and the independent variables represent, respectively, the energy consumption bills ( ${E n g}_{e l})$ , the natural gas consumption bills ( ${E n g}_{g a z})$ , the type of energy used for the heater ( ${E n g}_{e q b})$ , type of energy used to heat water ( ${E n g}_{e q c})$ , the energy used for cooking ( ${E n g}_{e q d})$ , the number of the appliances in the house ( ${E n g}_{a p p})$ , which one of the ovens, microwaves, washing machines, televisions, and laptops the selected French citizens have ( ${E n g}_{a p p b i s})$ , the frequency of action in terms of turning on/off of appliance ( ${E n g}_{g e s t})$ , the conditions in which the heater is turned off in winter in the household ( ${E n g}_{c h a u f})$ , the condition in which the household reduces the heater ( ${E n g}_{t e m p})$ .

D E C H = φ_{1} {D e c h}_{r e} + φ_{2} {D e c h}_{r e_d i s p} + φ_{3} {D e c h}_{r e c_pr} + φ_{4} {D e c h}_{s a c}

(6)

The dependent variable DECH represents the waste index, and the independent variables indicate respectively, the type of waste sorted ( ${D e c h}_{r e})$ , the type of recyclable waste making part of a selective pick up in the town ( ${D e c h}_{r e_d i s p})$ , represents if the following wastes are sorted: glass bottles, aluminium, iron or steel boxes, plastic bottles, paper/boxes, and alimentary trash $({D e c h}_{r e c_pr})$ , the quantity of non-sorted waste thrown per week ( ${D e c h}_{s a c})$

T R A N = \partial_{1} {T r a n}_{d t} + \partial_{2} {T r a n}_{d t a} + \partial_{3} {T r a n}_{d t t c} + \partial_{4} {T r a n}_{d v m}

(7)

The dependent variable TRAN is the transportation index, and the following independent variables represent respectively the transportation used to go to work ( ${T r a n}_{d t})$ , the transportation used to accompany kids, or family members to school or the workplace ( ${T r a n}_{d t a})$ , the reasons why they do not usually use public transportation ( ${T r a n}_{d t t c})$ , the transportation mode used to go to a far place from home for at least one night during the last months ( ${T r a n}_{d v m})$

VAR model

Y_{t} = α + A_{1} Y_{t - 1} + \dots + A_{p} Y_{t - p} + ε_{t}

(8)

Equation (8) describes the general form of our VAR model where A denotes the matrix of vector coefficients of variables, P the number of lags, and $ε_{t}$ the vector of error terms. The specified models are presented by equations (9) to (15), with $u_{1 t}$ to $u_{7 t}$ the error term.

{A T T}_{t} = β_{i} {\sum_{i = 1}^{k} A T T}_{t - i} + δ_{j} \sum_{j = 1}^{k} {A L}_{t - j} + γ_{m} {\sum_{m = 1}^{k} E C O}_{t - m} + θ_{p} {\sum_{p = 1}^{k} H A B}_{t - p} + φ_{q} {\sum_{t = q}^{k} E N G}_{t - q} + ω_{s} {\sum_{t = s}^{k} D E C H}_{t - s} + \partial_{z} {\sum_{t = z}^{k} T R A N}_{t - z} + u_{1 t}

(9)

{A L}_{t} = β_{i} {\sum_{i = 1}^{k} A L}_{t - i} + δ_{j} \sum_{j = 1}^{k} {A T T}_{t - j} + γ_{m} {\sum_{m = 1}^{k} E C O}_{t - m} + θ_{p} {\sum_{p = 1}^{k} H A B}_{t - p} + φ_{q} {\sum_{t = q}^{k} E N G}_{t - q} + ω_{s} {\sum_{t = s}^{k} D E C H}_{t - s} + \partial_{z} {\sum_{t = z}^{k} T R A N}_{t - z} + u_{2 t}

(10)

{E C O}_{t} = β_{i} {\sum_{i = 1}^{k} E C O}_{t - i} + δ_{j} \sum_{j = 1}^{k} {A T T}_{t - j} + γ_{m} {\sum_{m = 1}^{k} A L}_{t - m} + θ_{p} {\sum_{p = 1}^{k} H A B}_{t - p} + φ_{q} {\sum_{t = q}^{k} E N G}_{t - q} + ω_{s} {\sum_{t = s}^{k} D E C H}_{t - s} + \partial_{z} {\sum_{t = z}^{k} T R A N}_{t - z} + u_{3 t}

(11)

{H A B}_{t} = β_{i} {\sum_{i = 1}^{k} H A B}_{t - i} + δ_{j} \sum_{j = 1}^{k} {A T T}_{t - j} + γ_{m} {\sum_{m = 1}^{k} E C O}_{t - m} + θ_{p} {\sum_{p = 1}^{k} A L}_{t - p} + φ_{q} {\sum_{t = q}^{k} E N G}_{t - q} + ω_{s} {\sum_{t = s}^{k} D E C H}_{t - s} + \partial_{z} {\sum_{t = z}^{k} T R A N}_{t - z} + u_{4 t}

(12)

{E N G}_{t} = β_{i} {\sum_{i = 1}^{k} E N G}_{t - i} + δ_{j} \sum_{j = 1}^{k} {A T T}_{t - j} + γ_{m} {\sum_{m = 1}^{k} A L}_{t - m} + θ_{p} {\sum_{p = 1}^{k} E C O}_{t - p} + φ_{q} {\sum_{t = q}^{k} H A B}_{t - q} + ω_{s} {\sum_{t = s}^{k} D E C H}_{t - s} + \partial_{z} {\sum_{t = z}^{k} T R A N}_{t - z} + u_{5 t}

(13)

{{D E C H}_{t} = β}_{i} {\sum_{i = 1}^{k} D E C H}_{t - i} + δ_{j} \sum_{j = 1}^{k} {A T T}_{t - j} + γ_{m} {\sum_{m = 1}^{k} A L}_{t - m} + θ_{p} {\sum_{p = 1}^{k} E C O}_{t - p} + φ_{q} {\sum_{t = q}^{k} E N G}_{t - q} + ω_{s} {\sum_{t = s}^{k} H A B}_{t - s} + \partial_{z} {\sum_{t = z}^{k} T R A N}_{t - z} + u_{6 t}

(14)

{{T R A N}_{t =} β}_{i} {\sum_{i = 1}^{k} T R A N}_{t - i} + δ_{j} \sum_{j = 1}^{k} {A T T}_{t - j} + γ_{m} {\sum_{m = 1}^{k} A L}_{t - m} + θ_{p} {\sum_{p = 1}^{k} E C O}_{t - p} + φ_{q} {\sum_{t = q}^{k} H A B}_{t - q} + ω_{s} {\sum_{t = s}^{k} D E C H}_{t - s} + \partial_{z} {\sum_{t = z}^{k} E N G}_{t - z} + u_{7 t}

(15)

OLS

{E C O}_{t} = α + β_{1} {A T T}_{t} + β_{2} {A L}_{t} + β_{3} {H A B}_{t} + β_{4} {E n g}_{t} + β_{5} {D E C H}_{t} + β_{6} {T R A N}_{t} + ε_{t}

where

{E C O}_{t}

is the dependent variable,

{A T T}_{t}

β_{2} {A L}_{t}

{H A B}_{t}

{E n g}_{t}

{D E C H}_{t}

, and

{T R A N}_{t}

the independent variables and

ε_{t}

the error term of the model.

Hypothesis

The main objective of this paper is to analyse the nature of the relationship between French citizens’ consumption behaviour and environmental concerns.

The study aims to distinguish the French citizens’ daily consumption habits that lead to environmental degradation on the one hand, from those that contribute to environmental improvement, on the other hand, using the VAR model. Further, to capture the effectiveness of the relationship between the French citizens’ consumption behaviour and the environment, we employ the OLS estimation method as it helps to confirm the significance of the relationship and formulate therefore appropriate policy recommendations. Thus, the following hypotheses are tested:

i. The attitude index, Aliment index, and Energy index have either positive or negative impacts on environmental improvement.

ii. The waste index (Dech_index) and the Transportation index have a negative impact on environmental improvement.

Empirical results

Results of the subsystems combined indexes using the PCA

Tables 2 and 3 show the results of the principal component analysis of the ‘attitude subsystem’, we can observe from the results of eigenvalue estimation in Table 2 that only the first component has an eigenvalue above 1 and explains about 68% of the variation in the subsystem. The results of the scree plot test in Figure 1 confirm that only the first component should be retained to construct the attitude index while Table 3 shows that the highest correlation of components with the principal component belongs to attitude towards environmental problems (att_prob) followed by attitude towards the environment (att_nap) and finally attitude towards environmental solutions (att_sol), according to the calculation of component 1.

Table 2.

Estimation of eigenvalue for attitude subsystem.

Component	Eigenvalue	Difference	Proportion	Cumulative
Comp1	2.045	1.518	0.681	0.681
Comp2	0.527	0.099	0.175	0.857
Comp3	0.427		0.142	1

Source : Authors’ estimation.

Table 3.

Estimation of coefficients for the principal component of attitude subsystem.

Variable	Comp1	Comp2	Comp3
att_nap	0.583	−0.464	0.66
att_prob	0.589	−0.321	−0.74
att_sol	0.558	0.825	0.085

Source : Authors’ estimation.

Figure 1.

Scree plot attitude towards environment questions subsystem.

Tables 4 and 5 present the results of the principal component analysis of the ecology subsystem, it is highlighted in Table 4 the results of eigenvalue estimation, according to the criterion of eigenvalue above 1 we have two components retained and they explain together for about 60% of the variation in the model and this result is confirmed through the scree plot test in Figure 2. Besides, table 5 describes that the highest correlation of components with the principal component goes respectively to talking about the environment with friends (att_peco2), talking about the environment with family members (att_peco1), talking about the environment with colleagues (att_peco3), making part of an organization other than sports (att_orgeco), and finally considering yourself as a polluter (att_poll), while considering the calculation of component 1 and for the component 2, considering yourself as a polluter and being part of non-sport organizations have respectively the highest weight.

Table 4.

Estimation of eigenvalue for ecology subsystem.

Component	Eigenvalue	Difference	Proportion	Cumulative
Comp1	1.994	0.98	0.399	0.399
Comp2	1.014	0.102	0.202	0.601
Comp3	0.911	0.284	0.182	0.784
Comp4	0.626	0.174	0.125	0.909
Comp5	0.452		0.09	1

Source : Authors’ estimation.

Table 5.

Estimation of coefficients for the principal component of the ecology subsystem.

Variable	Comp1	Comp2	Comp3	Comp4	Comp5
Att_pEco1	0.561	−0.074	−0.063	−0.571	0.591
Att_pEco2	0.581	−0.183	−0.023	−0.196	−0.767
Att_pEco3	0.516	−0.21	−0.065	0.791	0.241
Att_OrgEco	0.23	0.56	0.792	0.064	−0.0003
Att_Poll	0.164	0.776	−0.602	0.066	−0.058

Source : Authors’ estimation.

Figure 2.

Scree plot ecology subsystem.

Table 5 estimation of coefficients for the principal component of the ecology subsystem Tables 6 and 7 highlight the results of the principal component analysis of the aliment subsystem, it can be seen in Table 6 the results of eigenvalue estimation, and according to the criterion of eigenvalue above 1 we have three components retained and they explain together for about 69% of the variation in the data and this result is confirmed by the scree plot test in Figure 3. Besides, Table 7 concludes that the highest correlation of components with the principal component appertain respectively to the criteria taken to account to buy products (al_mot), places where products are bought (al_ach), products got for free (al_acq), products thrown (al_jet), and products eaten (al_mang) while considering the first component.

Table 6.

Estimation of eigenvalue for aliment subsystem.

Component	Eigenvalue	Difference	Proportion	Cumulative
Comp1	1.431	0.416	0.286	0.286
Comp2	1.014	0.013	0.203	0.489
Comp3	1.001	0.066	0.2	0.689
Comp4	0.934	0.317	0.186	0.876
Comp5	0.617		0.123	1

Source : Authors’ estimation.

Table 7.

Estimation of coefficients for the principal component of aliment subsystem.

Variable	Comp1	Comp2	Comp3	Comp4	Comp5
Al_mot	0.666	0.169	0.059	−0.162	0.705
Al-ach	0.665	0.185	0.105	−0.107	−0.706
Al_acq	0.306	−0.475	−0.111	0.817	0.022
Al_jet	−0.122	0.326	0.866	0.352	0.045
Al_mang	−0.065	0.777	−0.470	0.412	0.009

Source : Authors’ estimation.

Figure 3.

Scree plot of aliment subsystem.

Tables 8 and 9 present the results of the principal component analysis of the clothing (hab) subsystem, it can be depicted in Table 8 the results of eigenvalue estimation, and according to the criterion of eigenvalue above 1 only the first component is retained and it explains for about 54% of the variation in the data and this result is confirmed by the scree plot test in Figure 4. Moreover, table 7 concludes that the highest correlation of components with the principal component belongs equally to the type of clothes and shoes bought for the last 12 months (hab_vet) and the number of shoes bought for the last 12 months (hab_ch) while considering the component 1.

Table 8.

Estimation of eigenvalue for clothing subsystem.

Component	Eigenvalue	Difference	Proportion	Cumulative
Comp1	1.072	0.144	0.536	0.536
Comp2	.927		0.463	1

Source : Authors’ estimation.

Table 9.

Estimation of coefficients for the principal component of the clothing subsystem.

Variable	Comp1	Comp2	Unexplained
Hab_vet	0.707	0.707	0
Hab_ch	0.707	−0.707	0

Source : Authors’ estimation.

Figure 4.

Scree plot of clothing subsystem.

Tables 10 and 11 show the results of the principal component analysis of the energy subsystem, it can be depicted in table 9 the results of eigenvalue estimation, and according to the criterion of eigenvalue above 1, four components are retained and they explain together for about 56% of the variation in the data and this result is confirmed by the scree plot test in Figure 5. Additionally, Table 11 highlights that the highest correlation of components with the principal component belongs respectively to the type of energy used to heat water (eng_eqc), type of energy used for the heater (eng_eqb), energy used for cooking (eng_eqd), frequency of ‘turn off the light when leaving a place in the house, using a washing machine with once clothes are a lot, not leaving television or laptop on sleep mode, dry clothes with air’ (eng_gest), which of some selected appliance French people (eng_appbis), conditions in which the heater is turned off during winter (eng_chauf), electricity energy, natural gas energy, number of home appliance in the house (eng_app), in which condition the heater is lowered (eng_temp) while considering the component 1.

Table 10.

Estimation of eigenvalue for energy subsystem.

Component	Eigenvalue	Difference	Proportion	Cumulative
Comp1	2.079	0.623	0.208	0.208
Comp2	1.456	0.349	0.145	0.353
Comp3	1.107	0.072	0.110	0.464
Comp4	1.035	0.124	0.103	0.567
Comp5	0.910	0.014	0.091	0.658
Comp6	0.895	0.112	0.089	0.748
Comp7	0.782	0.066	0.078	0.826
Comp8	0.715	0.184	0.071	0.898
Comp9	0.531	0.044	0.053	0.951
Comp10	0.486		0.048	1

Source : Authors’ estimation.

Table 11.

Estimation of coefficients for the principal component of energy subsystem.

Variable	Comp1	Comp2	Comp3	Comp4	Comp5	Comp6	Comp7	Comp8	Comp9
Eng_El	0.128	0.691	0.039	0.045	0.034	0.011	−0.077	0.022	−0.644
Eng_Gaz	0.109	0.691	0.007	0.064	−0.133	0.039	0.057	0.025	0.650
Eng_Eqb	0.450	−0.060	0.128	−0.190	0.086	0.266	0.542	−0.397	0.140
Eng_Eqc	0.519	−0.074	0.236	−0.174	−0.077	0.216	−0.058	−0.066	−0.277
Eng_Eqd	0.423	−0.134	0.254	−0.052	−0.365	0.027	−0.534	0.334	0.187
Eng_App	0.056	−0.104	−0.163	0.807	−0.285	0.460	0.091	−0.019	−0.073
Eng_Appbis	0.320	−0.062	−0.049	0.234	−0.271	−0.740	0.416	0.177	−0.082
Eng_Gest	0.349	−0.023	−0.254	0.291	0.450	−0.283	−0.448	−0.481	0.108
Eng_Chauf	0.301	−0.025	−0.542	−0.096	0.414	0.197	0.119	0.615	0.026
Eng_Temp	−0.023	−0.012	0.687	0.351	0.550	−0.027	0.101	0.287	0.073

Source: Authors’ estimation.

Figure 5.

Scree plot of energy subsystem.

Tables 12 and 13 display the results of the principal component analysis of the waste (DECH) subsystem, it can be depicted in Table 12 the results of eigenvalue estimation, and according to the criterion of eigenvalue above 1, 2 components are retained and they explain together for about 79% of the variation in the data and this result conforms to the scree plot test in Figure 6. Additionally, Table 13 highlights that the highest correlation of components with the principal component belongs respectively to the sorting of a type of waste (dech_re), type of selective waste making part of the pickup in the town (dech_re_disp), sorting the following ‘glass bottles, aluminium, iron or steel boxes, plastic bottles, paper/boxes, alimentary trash’ (dech_rec_pr), and quantity of no sorted waste throw per week (dech_sac) while considering the component 1.

Table 12.

Estimation of eigenvalue for waste subsystem.

Component	Eigenvalue	Difference	Proportion	Cumulative
Comp1	2.094	1.016	0.523	0.523
Comp2	1.077	0.250	0.269	0.793
Comp3	0.827	0.827	0.206	1
Comp4	0		0.000	1

Source: Authors’ estimation.

Table 13.

Estimation of coefficients for the principal component of the waste subsystem.

Variable	Comp1	Comp2	Comp3
Dech_Re	0.674	−0.198	0.075
Dech_Re_Disp	0.674	−0.198	0.075
Dech_Rec_Pr	0.259	0.590	−0.764
Dech_Sac	0.151	0.757	0.635

Source: Authors’ estimation.

Figure 6.

Scree plot of waste subsystem.

Tables 14 and 15 display the results of the principal component analysis of the transportation subsystem, it can be illustrated in Table 14 the results of eigenvalue estimation, and according to the criterion of eigenvalue above 1, 2 components are taken and they explain together for about 64% of the variation in the data and this result conforms to the scree plot test in Figure 7. Additionally, table 15 highlights that the highest correlation of components with the principal component belongs respectively to the type of transportation used ‘to accompany kids, or family members to school or workplace’ (tran_dta), transportation used to go to a far place from home for at least one night (tran_dvm) while considering the component 1.

Table 14.

Estimation of eigenvalue for transportation subsystem.

Component	Eigenvalue	Difference	Proportion	Cumulative
Comp1	1.553	0.525	0.388	0.388
Comp2	1.028	0.065	0.257	0.645
Comp3	0.962	0.507	0.240	0.886
Comp4	0.455		0.113	1

Source: Authors’ estimation.

Table 15.

Estimation of coefficients for the principal component of transportation.

Variable	Comp1	Comp2	Comp3	Comp4
Tran_Dt	−0.021	0.750	−0.660	0.010
Tran_Dta	0.696	−0.091	−0.116	0.701
Tran_Dttc	−0.703	0.015	0.052	0.708
Tran_Dvm	0.139	0.654	0.740	0.069

Source: Authors’ estimation.

Figure 7.

Scree plot of transportation subsystem.

Results of the impulse response function

This method is used to capture the impact of shocks in a variable on the variable itself and other variables of the VAR model in the short and long run. This research examines the impact of shock in 5 periods. Figure 8 shows the response to the shocks of ecology in the VAR model. The first picture shows that a standard deviation shock in the ecology index has a positive impact on itself of about 1.39 in the first period, then negative in the second period (−0.021), positive (0.04), and negative (−0.003) from periods 2 to 4 before seeing its impact being null in the last period. This implies that environmental protection policies have a positive impact only in the short run but in the long run, there is no significant impact. The 2^nd picture shows the response of the attitude index to a standard deviation shock in the ecology index. It can be seen that there is no effect in the first period, then in the second period there is a slightly positive impact (0.04) then this positive impact decreases gradually in the next periods. This shows that ecological questions have no significant impact on the attitude of the French in the long run even if few positive effects are observed in the short term. Picture 3 highlights the response of the aliment index to ecology, there is no impact in period 1, and a slightly negative impact in periods 2, 3, and 5 while period 4 is slightly positive. The polluting aliments consumed by French citizens are somewhat decreasing in the short run; however, in the long run, ecological concerns have no significant impact on French alimentation. Pictures 4, 5, and 6 show that ecological shocks first harm clothing, energy, and waste, before having a positive impact and finally no significant impact in the long run while the positive impact of ecological shocks on transport is observed in the short and long run even if the effect is very small in long run. It can be concluded that ecological concerns seem to show some significant improvement only in the transportation sector.

Figure 8.

Results of impulse response function to ecological shocks.

Figure 9 shows the response to the standard deviation shock of the attitude index in the VAR model. The first picture shows the response of the ecology index. It can be observed that a standard deviation shock in the attitude index has a positive impact on ecology in periods 1 (0.6), 2 (0.02), and 3 (0.0008), and in the last period; the only negative impact is for period 4. This implies that an improvement in the attitude towards environmental questions leads to an improvement of the environment in the short and long run even if the positive impact, in the long run, is smaller. The second picture gives the response of attitude shock to itself and there is a positive impact in the short run (1.29) but this impact is gradually diminishing till becoming close to zero in the long run. The third picture displays the response of the aliment index to attitude shock. There is no impact in period 1, very little impact is seen in period 2 (0.04), while period 3 shows little negative impact (−0.028) and for periods 4 and 5 the impact is almost null. This highlights that there is no impact of the attitude index on the aliment index in both the short and long run. The attitude shock has also no impact on the clothing (hab) index, energy index, waste (dech) index, and transportation index in both the short and long run since the first and last periods have null coefficients.

Figure 9.

Results of impulse response function to attitude shocks.

Figure 10 shows the response to the standard deviation shock of the aliment index in the VAR model. The first picture shows the response of the ecology index. It can be observed that a standard deviation shock in the aliment index has a positive impact on ecology in periods 1 (0.5), slightly negative in periods 2 (−0.012) and 3, and a positive and very small impact in 4 and 5. It concludes that an improvement in aliment eaten has a positive impact on the environment in the short and the impact, in the long run, is almost equal to zero. The second picture shows the impact on the attitude index, it is observed that a standard deviation shock in the aliment index has a positive impact on the attitude in the short run through the value of the first (0.3) and second (0.011) periods but in the long run, this positive impact is almost equal to zero. Picture 3 shows that a standard deviation shock in aliment has a positive impact on itself in the short run (period 1 (1.04) and no impact in the long run (periods 4 and 5). From the fourth to the seventh picture, we can see that foods (aliment) eaten have no impact on clothing (hab), energy, waste (dech), and transportation in the short and long run.

Figure 10.

Results of impulse response function to aliment shocks.

Figure 11 shows the response to the standard deviation shock of clothing (hab) index in the VAR model. The first picture shows the response of the ecology index. It can be observed that a standard deviation shock in the clothing index has a positive impact on ecology in periods 1 (0.084), slightly negative in periods 2 (−0.043) and 3 (−0.017), and a very small positive impact in periods 4 and 5. It implies that an improvement in the type of clothing has a positive impact on the environment in the short and the impact, in the long run, is almost equal to zero. The second picture shows the impact of clothing on the attitude index; it is observed that a standard deviation shock in the clothing index has a positive impact on attitude in the first period (0.073) and a very small negative impact from periods 2 to 4 and null in period 5. It implies that standard deviation shock in the clothing index has a positive impact on attitude in the short run and this impact is decreasing till getting close to zero in the long run. Picture 3 shows that a standard deviation shock in clothing has a positive impact on the aliment index in period 1 (0.12) and from period 2 to 5 the impact is almost equal to zero. Picture 4 shows the impact of clothing shock on itself. It can be seen that in the short run, the impact is positive and equals zero in the long run. Pictures 5 to 7 show that a standard deviation shock in clothing has no impact in the short run and long run according to the observation of the first and the last period.

Figure 11.

Results of impulse response function to clothing (HAB) shocks.

Figure 12 shows the response to the standard deviation shock of the energy index in the VAR model. The first picture shows the response of the ecology index. It can be observed that a standard deviation shock in the energy index has a positive impact on ecology in periods 1 (0.23) and 2 (0.026), a slightly negative impact in periods 3, and an almost null impact in periods 4 and 5. It implies that an improvement in energy management in the house has a positive impact on the environment in the short run and no impact in the long run. The second picture shows the impact of energy on the attitude index; it can be observed that a standard deviation shock in the energy index has a positive impact on attitude in the first period (0.18) and a very small negative impact from periods 2 to 5. It implies that standard deviation shock in the energy index has a positive impact on attitude in the short run and this impact becomes negatively close to zero in the long run. Picture 3 shows the impact of energy on aliment, it is seen that a standard deviation shock in energy has a positive impact on the aliment index in period 1 (0.15), and from period 2 (0.1), the impact becomes negative in period 3 and almost equals to zero in period 4 and 5. Picture 4 shows the impact of energy shock on the clothing index. It is described that in the short run, the impact is positive (period 1) and equals zero in the long run (periods 4 and 5). Picture 5 shows that a standard deviation shock in energy has a positive impact on itself in the short run and this impact decreases first in the second period before becoming negatively close to zero in the long run. The shock of energy index has no impact on waste (picture 6) and transportation (picture 7) in both the short and long run.

Figure 12.

Results of impulse response function to energy shocks.

Figure 13 shows the response to the standard deviation shock of the waste index in the VAR model. The first picture shows the response of the ecology index. It can be detected that a standard deviation shock in the waste index has a positive impact on ecology in period 1 (0.073) and negative in period 2 (−0.07), positively close to zero from periods 3 to 5. The second picture shows the impact of waste on the attitude index; it is displayed that a standard deviation shock in the waste index has a positive impact on attitude in the first period (0.12) and a very small negative impact in the second (−0.072) and third (−0.017) periods while periods 4 and 5 impact almost equal to zero. It implies that standard deviation shock in waste index has a positive impact on attitude in the short run and this impact becomes positively close to zero in the long run. Picture 3 shows the impact of the waste index in aliment, it is seen that a standard deviation shock in waste has a positive impact in the short term on the aliment index (periods 1 (0.12) and 2 (0.018)), and in the long run the impact of waste on aliment is negatively close to zero (periods 3 to 5). Picture 4 shows the impact of the waste shock on the clothing index. It is described that in the short run, the impact is negative (period 1) and equals zero in the long run (periods 4 and 5). Picture 5 shows that a standard deviation shock in waste has a positive impact on energy in the short run (periods 1 to 3) and this becomes negatively close to zero in the long run (periods 4 and 5). Picture 6 shows that the impact of the waste shock on itself is positive in the short run and close to zero in the long run. Picture 7 displays no impact of waste on transportation in the short run (period 1), a negative impact is seen in periods (2 and 3) while the impact, in the long run, is positively close to zero (picture 7).

Figure 13.

Results of impulse response function to waste (DECH) shocks.

Figure 14 shows the response to the standard deviation shock of the transportation index in the VAR model. The first picture shows the response of the ecology index. It can be noticed that a standard deviation shock in the transportation index harms ecology in periods 1 (−0.074), 3(-0.069), and 5 (−0.0026), while a positive impact is identified in periods 2 (0.017) and 4 (0.001). It indicates that the impact of transportation on the ecology is negative in both the short and long run. The second picture shows the impact of transportation on the attitude index; it is exhibited that a standard deviation shock in the transportation index has a positive impact on attitude in the first period (0.13) and slightly in the last period (0.00029) while a very negative impact is perceived in periods 2, 3, and 4. It means a shock in transportation has a positive impact on attitude in both the short and long run. Picture 3 shows the impact of the transportation index in aliment, it is seen that a standard deviation shock in transportation has a positive impact on the aliment index. This positive impact decreases with time highlighting a positive impact in the short and long run with the impact of the long run relatively close to zero. Picture 4 shows the impact of transportation shock on the clothing index. It detected a negative impact from periods 1 to 5. This highlights the negative impact of transportation on clothing in both the short and long run. Picture 5 shows that a standard deviation shock in transportation has a positive impact on energy in the short run (periods 1 to 3) and an impact negatively close to zero in the long run (periods 4 and 5). Picture 6 shows that the impact of the waste shock on itself is positive in the short run and close to zero in the long run. Picture 7 displays a positive impact of transportation on itself in periods 1, 2, 3, and 5 while the impact of period 4 is negative. This highlights the positive impact of transportation on itself in both the short and long run.

Figure 14.

Results of impulse response function to transportation shocks.

OLS estimation results

Since all variables are integrated at level there is no need to perform the cointegration test because any shock to the system in the short run quickly adjusts to the long run. Consequently, only the long-run model should be estimated using the OLS. Table 16 presents the results of the OLS estimation. It displays a negative and statistically significant correlation between ecology and transportation. This shows the negative impact of carbon dioxide (CO₂) emission on the environment captured by the car pollution of French households. The empirical results highlight as well the negative impact of waste on the environment. An increase of 1 unit in waste in French households leads to environmental degradation by 0.0215. This shows the negative impact that household waste-related pollution can have on the environment. Besides, a positive relationship between environment and attitude is also highlighted.

Table 16.

Estimation results of the impact of French lifestyles on the environment.

Variables	(OLS)
Variables	ECOL_index
ATT_Index	0.297***
ATT_Index	(0.0190)
AL_index	0.344***
AL_index	(0.0230)
HAB_index	−0.00457
HAB_index	(0.0242)
ENG_index	0.0566***
ENG_index	(0.0178)
DECH_index	−0.0215**
DECH_index	(0.0174)
TRANS_index	−0.126***
TRANS_index	(0.0199)
Constant	−4.42 × 10⁻⁹
Constant	(0.0246)
Observations	2362
R-squared	0.267

Source: Authors’ estimation, the dependent variable capturing the environmental dimension is the index of the ecology, and independent variables are the attitude index, aliment index, clothing index (hab_index), energy index, waste index (dech_index), and transportation index.

Standard errors in parentheses.

***p < 0.01, **p < 0.05, *p < 0.1.

We can see in Table 16 that when the attitude towards environment questions increases by 1 unit it conducts to an improvement of the environment by 0.297. Moreover, the type of foods eaten by the French household has a positive impact on the ecology. Furthermore, a positive and statistically significant relationship is highlighted between ecology and the use of energy in French households. This shows that French citizens’ habits towards energy use are to save the use of energy. This is quite pertinent as the cost of electricity is high in France, people tend to reduce the use of energy even during winter in order to minimise their monthly bills related to electricity use. This contributes to an improvement in the energy use highlighting an optimum management of energy use by French citizens leading to improving the environment.

Discussions

To manage the scope, complexity, and uncertainty of climate change problems, it is important to take account of different types of environmental factors (Bohringer and Rosendahl, 2022; Pojani and Stead, 2018; Wei et al., 2007; Xu et al., 2016; Şimşekoğlu et al., 2015). Some scientists interested in Carbon dioxide (CO₂) and global warming argue that western paradigms and systems of knowledge are currently not able to deal with the full complexity of environmental management (Gorges and Holz-Rau, 2021; Olsson and Folke, 2001; Raymond et al., 2010; Reid et al., 2006), nor sufficiently able to integrate stakeholder perspectives in the development of environmental management strategies (Geels, 2005; Olsson and Folke, 2001; Raymond et al., 2010; Reid et al., 2006). The current challenge for researchers and policymakers is to find new and useful management approaches to better manage environmental factors (Ecology, Aliment, Clothing, Energy, Waste, and Transportation) in order to positively influence all the stakeholders concerned with global warming and climate change. In response to these challenges, we went beyond economic approaches to propose a managerial analysis of the environment that takes into consideration the attitudes and behaviours of citizens. By doing so, we investigated citizens’ behaviours and a range of environmental factors in order to design and roll out appropriate environmental policies to combat climate change. Our results highlight the importance of addressing the impact of lifestyle on the environment in France and confirm the positive relationship between the environment and citizens’ attitudes. These results provide new theoretical implications for scholars and new practical implications for policymakers.

Theoretical implications

Previous research has considerably improved our understanding of the impact of personal lifestyle on environmental change (Li et al., 2019; Xu et al., 2016), leaving open the question of French citizens’ behaviour towards the environment. Through an economic perspective, a great deal of authors (Bohringer and Rosendahl, 2022; Li et al., 2019; Xu et al., 2016; Yang et al., 2016; Şimşekoğlu et al., 2015) attempted to approach the question in many countries. Although these authors have provided answers to the adverse impacts of global trends on the environment and natural resources, they have, however, shown the weakness of taking into consideration only part of the problem. To fill this gap, this paper has opted for a broader approach focussing attention, not only on an economic approach, but also on a managerial approach in order to enrich previous works by taking account of citizens' behaviour towards environment. Our VAR results demonstrated that a shock in attitude index has a positive impact on the environment. This implies that French citizens seem to have a good attitude towards the protection of the environment in the both short and long run. Furthermore, while VAR results show a positive attitude towards the protection of the environment, the OLS results confirm that positive relationship between environment and attitude (Bøhlerengen and Wiium, 2022; Duchin, 1998; Rees, 1995; Schipper et al., 1989). This shows that environmental concerns seem to be integrated into the French citizens’ behaviour, highlighting that French people are open to accepting the cost of implementing some policies that could improve the protection of the environment. Positive attitudes and environmental concerns have a direct effect on sustainable consumption behaviour (Saari et al., 2021). We can see in Table 16 that when the attitude towards environment questions increases by 1 unit it conducts to an improvement of the environment by 0.297. Our findings are in line with prior findings (Buttel, 1987; Dunlap and Catton, 1994; Gjerstad and Fløttum, 2021; Lazaric et al., 2020; Von Borgstede et al., 2013) that stated that the behaviour towards the environment is a crucial factor when addressing the protection of the environment. Therefore, environmental concern is shown to strongly influence behavioural attitude, which further mediates sustainable behaviour as shown by Saari et al. (2021). Citizens’ attitude towards the environment seems to be an important variable in predicting sustainable behaviour. This finding is in line with recent research on the roles of motives in individuals’ attitudes to adopt and apply environmental behaviour (Gkargkavouzi et al., 2019; Lazaric et al., 2020; Gjerstad and Fløttum, 2021; Bøhlerengen and Wiium, 2022). Our results support as well the study by Bøhlerengen and Wiium (2022) who concluded that, in the Norwegian context, environmental attitudes and environmental knowledge can influence people’s decision-making process towards environmental protection and it is an important factor in improving environmental behaviours. Similar findings have been reported by earlier studies, which show that citizens aware of the possible damaging effects of their behaviour tend to have a more positive attitude towards the protection of the environment (Bohringer and Rosendahl, 2022; Gjerstad and Fløttum, 2021).

Regarding the standard deviation shock of the aliment index on the environment, our VAR model highlights a positive relationship in 3 periods out of 5 (periods 1, 4, and 5) and a negative relationship in the two other periods (2 and 3). It concludes with the mixed impact of French citizens’ food consumption on the environment. The OLS estimate displayed a positive and statistically significant impact of the type of foods eaten by French households on the environment. It shows that French citizens adapt gradually their consumption habits to organic foods. Hence, an increase of organic foods eaten by 1 unit improves the environment by 0.344 showing that the use of organic food complying with the standards of organic farming contributes to the protection of the environment. According to our findings, the gradual adaptation of citizens’ consumption habits to organic foods is also important in explaining sustainable consumption behaviour. These results are contrary to Nicholson and Snyder (2011) who suggested that economic activity of consumption and production involves higher economic output leading to greater waste and environmental pollution. More interesting, is the effect of the type of foods eaten by the French household. In fact, the type of food has a positive impact on the chances of adopting sustainable behaviours, which suggests that scholars should also direct their research towards themes that have links to the consumption of organic products, sustainable agriculture and food processing to protect natural ecosystems, as well as the production of high-quality nutritional foods that prevent harm to human health. Concern for organic food would seem to be a « luxury », especially for low-income citizens that struggle to adjust their budgets to the economic constraints. In this context, sustainable consumption appears to be of secondary importance compared to other issues (Lazaric et al., 2020), although this study shows a positive and statistically significant impact of organic foods. As mentioned by Paddock (2017) these results can provide a nexus point around which change can be more effectively conceptualized for public policies aimed at inculcating more sustainable ways of life in order to stimulate the demand for organic products which can be crucial for transforming agricultural production in more ecological directions.

According to our VAR model results, the response to the standard deviation shock of clothing (hab) index has a positive impact on ecology in periods 1 (0.084), slightly negative in periods 2 (−0.043) and 3 (−0.017), and a very small positive impact in periods 4 and 5. The VAR shows mitigated results of French citizens’ clothing on the environment. The OLS results highlight as well a negative relationship between clothing and environmental protection; however, the result is not statistically significant. The standard deviation shock of the energy index on the environment suggests a positive impact in periods 1 (0.23) and 2 (0.026), a slightly negative impact in periods 3, and an almost null impact in periods 4 and 5. Furthermore, the OLS results conclude a positive and statistically significant relationship between energy use and the environment. For instance, an improvement in the optimum use of energy by 1 unit will improve the ecology by 0.0566. These results are contrary to Maloney and Ward (1973) who acknowledged human lifestyles have intrinsic inappropriate characteristics relating to energy consumption, they stressed that the emissions of greenhouse gases (GHG) can lead to the destruction of human life. They also provide new robust results related to the relationship between waste and the environment, which complement prior findings (Bohringer and Rosendahl, 2022; Gjerstad and Fløttum, 2021; Lazaric et al., 2020; Von Borgstede et al., 2013).

Besides, the standard deviation shock of the waste index on the ecology index using the VAR model gives a positive impact of waste on the environment in period 1 (0.073) and negative in period 2 (−0.07), positively close to zero from periods 3 to 5. This shows mixed results regarding the relationship between the waste and the environment. However, the OLS estimate concludes with a negative impact of waste on the environment. In fact, an increase of 1 unit in waste in French households leads to environmental degradation by 0.0215. This result is statistically significant showing that the management of waste in France needs some improvement. The takeaway of this negative relationship is that French citizens did not yet integrate the culture of sorting the waste to some extent at least the ones interviewed. Therefore, as stated by Tilbury (1995) there is a need to change human behaviour through environmental education to protect the environment, environmental education associated with training contribute to improving the level of public awareness and knowledge (Tran, 2006). As far as transport is concerned, the standard deviation shock of the transportation index on the environment, the empirical results of our VAR model show that a shock in the transportation index has a negative impact on the environment in periods 1 (−0.074), 3(-0.069), and 5 (−0.0026) while a positive impact is identified in periods 2 (0.017) and 4 (0.001). It indicates that the impact of transportation on the environment is negative in both the short and long run. In fact, the results of the OLS displayed a negative and statistically significant correlation between ecology and transportation. This shows the negative impact of carbon dioxide (CO₂) emission on the environment captured by the car pollution of French households. Thus, an increase of 1 unit in road traffic leads to a degradation of the environment by 0.126. So, the preservation of the environment in France implies implementing government policies that can discourage the abusive use of individual vehicles.

Practical implications

Among the subsystems of attitude, Ecology, Aliment, Clothing, Energy, Waste, and Transportation, we observed positive effects on French citizens’ environmental behaviours, which is in line with the literature (Poortinga et al., 2004; Von Borgstede et al., 2013; Lazaric et al., 2020; Gjerstad and Fløttum, 2021). Our findings lead to two main managerial contributions. First, we add to the debate on environmental management linking to climate change by highlighting new consumption practices that are more likely to be diffused via environmental policies which are primarily concerned with how to govern the relationship between humans and the environment in a mutually beneficial manner. New consumption practices are seen as new practices that are perceived as having a nil, minimal or reduced impact on the environment, such as choosing environmentally appropriate materials, minimising the use of disposable or single use products, recycling and purchasing environmentally friendly products, and promoting organic products and foodstuffs. Second, we highlight specific policy implications that suggest a more holistic approach for mobility management that concerns the promotion of sustainable mobility as well as the management of private and public transport demand through changes in attitudes and behaviour of users. As far as the promotion of new consumption practices is concerned, our findings confirm existing literature related to climate change (Biesiot and Noorman, 1999; Bohringer and Rosendahl, 2022; Gjerstad and Fløttum, 2021; Wei et al., 2007) and provide new results that challenge us on the need to act more responsibly in everyday life to mitigate the negative impacts of climate change on humans and the environment. For instance, Su and Ang (2017) explained that improvements in living standards have increased consumer demand and thereby caused rapid expansion in energy requirements. French citizens’ environmental behaviours are essential for environmental conservation, hence the need to identify facilitating factors. Promoting positive and responsible new consumption practices among citizens may empower them to contribute actively to their environment through positive attitudes and behaviours. A lot of sustainable actions are viewed as effortful and time-consuming, which can be a reluctance to sustainable actions. Therefore, one strategy to encourage positive and sustainable practices is to make them easier to do. One means of making sustainable practices easier is to deal with sustainability engagement that goes beyond passive awareness or superficial gestures. Without greater policy effort, the negative impacts of households on the environment are likely to intensify over the coming years as populations and disposable incomes grow. Strategies that promote environmentally sustainable lifestyles and consumption patterns are urgently needed for both the citizen in his daily life and the government which designs public policies to act sustainably in favour of the protection of the environment. The government should take the lead in improving social welfare and making people more aware of environmental issues, such as climate change, resource conservation, and waste management. Policymakers should seek to raise awareness, encourage action, and motivate citizens to genuinely integrate sustainable behaviours into their daily lives. Contextual changes that improve the ease of engaging in sustainable behaviours, such as saving energy by using lighting correctly, keeping appliances switched off, and choosing energy-efficient appliances, placing recycling bins nearby, reducing waste, using sustainable transportation, and buying green products, encourage such behaviours. Another means of making sustainable actions easier is to encourage and promote sustainable behaviour by using « feedback ». This involves providing citizens with specific information about their own performance on behaviours. The government can provide feedback for actions like water and energy usage, and it can be provided with reference to the citizen’s own past behaviours.

According to previous studies, citizens calculate the costs and benefits of their actions from a self-interested perspective, and sustainable behaviour is not always regarded as beneficial to the individual (Menzel, 2013; Saari et al., 2021). Considering the fact that the self-interest of citizens has also been found to have a stronger impact on sustainable behaviours than environmental concern (Saari et al., 2021), the collaboration of all players is a prerequisite that must be systematically taken into account in decision-makers’ strategies. It is important to emphasize that these new consumption practices are not understood as individual activities, but rather as broader socially shared entities that individuals perform. In accordance with, decision-makers must have a better understanding of multiple stakeholders’ interests and influence in order to promote pro-environmental attitudes (Holian and Kahn, 2015; Yang et al., 2016), such as greater attitudes toward environmental protection and pro-environmental consumerism (Duchin, 1998; Rees, 1995). Moreover, the results of this research lead to a more general discussion on the protection of the environment and citizens’ well-being. They seem to reveal managerial interests and motivation to engage in general pro-environmental actions. Recommendations for new actions can then be proposed in order to overcome problems with which the various citizens are confronted. Indeed, regarding citizens’ behaviour as a key to protecting the environment offers a more interesting analysis of public policies. From the above analysis, we observe the significance of some variables for spurring sustainable consumption. While this is not a novel finding (Bin and Dowlatabadi, 2005; Gjerstad and Fløttum, 2021), we demonstrate that the management of waste needs some improvements, this should be further addressed by policies and environmental education initiatives. Existing tools such as incentives and information can be greatly enhanced in order to further facilitate the promotion of sustainable practices and enable the transition to a circular economy by using ‘hard’ instruments such as legislation and regulation to compel citizens to act in certain ways. In this context, penalties are essentially types of punishment that decrease the tendency to engage in undesirable behaviour. These penalties might take the form of a fine that can encourage behaviour change in domains that can be monitored like the disposal of waste (Gjerstad and Fløttum, 2021). Although coercive strategies can certainly deter unsustainable behaviours in some instances, policymakers should be aware that these same strategies can trigger backfire effects if the fine seems unreasonable or unfair. In addition to this coercive strategy, environmental campaigns and education material should refer to the gravity of the current environmental risks so that policymakers can create better social awareness regarding the importance of environmental protection. Given the shift in climate patterns mainly caused by greenhouse gas emissions from human activities, all the stakeholders should work together to educate the public and disseminate knowledge about environmental protection.

The diversity, efficiency, and cost of passenger transport are essential for the proper functioning of the French production system and labour market, for citizens to access education, training, health, and for social cohesion. In addition, French work to reduce greenhouse gas emissions relies critically on the transport sector, the main emitter of greenhouse gases. This study also provides a comprehensive view of the situation of transport in France and the conditions required to create capacities to make mobility management a vital tool for policymakers. In addition to the promotion of new consumption practices, policymakers should keep developing and implementing strategies to ensure the transport of people and goods efficiently, with regard to social and environmental aims. In this context, mobility management can result in a radical decrease in personal vehicles use and therefore reduce pollution related to carbon emissions. Our findings indicate that the impact of transportation on the environment is negative in both the short and long run. Our results are in line with previous studies that highlighted the negative impact of externalities on environmental degradation (Nicholson and Snyder, 2011; Yahoo and Othman, 2017). In accordance with this situation, a mobility management approach should be proposed by practitioners to develop sustainable transport and manage the demand for car use by changing citizens’ attitudes and behaviour. According to Ashmore et al. (2017), in the collective consciousness, private motorized vehicles have been long associated with pleasure, comfort, speed, convenience, power, protection, superiority, individuality, hedonism, and freedom. French citizens act the same way, and despite the efforts pursued by the French government to address the problems of transport (Fazey et al., 2006; Raymond et al., 2010), there is a need to ‘manage mobility’ properly in order to promote sustainable mobility as well as the management of private and public transport demand through changes in attitudes and behaviour of users. To tackle certain transport-related problems, such as pollution, oil dependency, traffic congestion, and accidents, new technologies can be a lever for policymakers (Pojani and Stead 2018) to positively influence citizens’ behaviour. In this context, mobility management can help find cleaner alternatives to diesel and gasoline and promote a wide range of vehicles (taxis, garbage trucks and buses) that can run on alternative fuels including electricity and biofuels. For example, the Chinese government has recently announced that it is working on a timetable to end production and sales of traditional energy vehicles (Pojani and Stead, 2018). According to a great deal of specialists, electric cars are the most advanced and most affordable solution available on today’s market to respond to environmental issues related to global warming and air pollution. Over the last decade a variety of support policies for electric cars were instituted in France which helped influence citizens’ behaviours and attitudes toward climate change. But our results indicate that the challenge remains enormous and policies to promote electric vehicle deployment will require new electric car incentives in addition to the ecological bonus² and the conversion bonus. The government introduced in 2024 a new environmental scoring system. From January 2024, it includes the carbon footprint of the materials used in its production, assembly, and transport. This new feature is also designed to give preference to vehicles manufactured and produced more locally (in France and Europe). To be eligible for this government aid, vehicles must score at least 60 points out of a total of 80. This score is based on the different carbon footprint categories: carbon footprint of ferrous metals, carbon footprint of aluminium, battery carbon footprint, carbon footprint of transport, carbon footprint of intermediate transformations and assembly, and carbon footprint of other materials. Designed to encourage people to buy vehicles with low or very low emissions, the French scoring system seems to be very difficult to understand. The government must simplify this system to allow a better understanding of the scoring process³. Rather than setting up a complex scoring system, policymakers should clearly determine the financial advantages when buying an electric vehicle. Indeed, mobility management should allow decision-makers to make things easier. Simplifying processes and solutions should be the main levers to act in favour of the environment and citizens’ well-being. For these electric car incentives to unleash their full potential to combat climate change, policymakers will need to propose an effective mobility management that will take into account a combination of weak administrative arrangements, limited planning capacity and a lack of coordination that inhibits the development of more innovative, integrated, and sustainable policies (Pojani and Stead, 2017).

Beyond these two main managerial implications, mobility management is part of environmental management which is an important tool for decision-makers. It needs to be sufficiently flexible to take into account changes in citizens’ behaviours and attitudes and to deal with new technology arising in transport industry. Moreover, mobility management need to be embedded in organisations which are flexible to deal with multiple forms of knowledge and environmental constraints, across multiple social and ecological dimensions that influence citizens’ lifestyle. Explicit in mobility management is that environmental factors such as transport and waste can support learning through government communication and implication.

Conclusions

Our empirical findings highlight the importance of addressing the impact of lifestyle on the environment in France and confirm the hypothesis of the positive relationship between environment and attitude. Based on a large French survey of sustainable consumption practices, we suggest that one of the most efficient measures for environmental protection is the change of lifestyles which is the transition from a carefree life without any particular constraints to a more responsible way of life. We also highlight new trends, notably the impact of lifestyle on climate change and the strong significance of lifestyle on transport, energy use, consumption, food waste, and clothing. From the above analysis, we observed that lifestyle issues, such as transport and food, have important impacts on the environment and household carbon emissions in France validating the hypothesis of a negative impact of both transport and waste on the environment in the context of French citizens lifestyle, on the one hand, and the one of a positive impact of food on environment, on the other hand. The only hypothesis that failed to be validated is regarding the negative impact of clothing on the environment. Therefore, it allows assessing the impact of lifestyle on environmental factors in order to manage the harmful effect of climate change on French citizens.

Despite the significant findings on the link between the impact of lifestyle and environmental factors, the present study presents some limitations. Whether the environmental factors are good enough to capture French citizens’ attitudes, behaviour, and responsibility for the environment is a topic that needs to be explored further in future studies with qualitative methods (Bøhlerengen and Wiium, 2022). As the present study used the PCA and VAR approaches, it can be interesting to investigate further the findings with qualitative methods and more up-to-date environmental factors. For example, it may be of interest to investigate environmental concerns regarding issues that deal with ecotourism, organic food, meat consumption, and France’s gold activities in French Guyana. Besides, the longitudinal study used in assessing French lifestyles and their behaviour towards the environment may not be valid or reliable enough. This is a limitation that needs to be addressed with more adequate measures in future research (Bøhlerengen and Wiium, 2022). Thus, our empirical findings could be extended to investigate certain variables, such as gender, age, and income, in more depth, to allow a better understanding of how and in what context these variables matter when citizens try to adopt sustainable behaviour (Lazaric et al., 2020). Furthermore, future works could also examine other factors that influence sustainable behaviour, such as the influence of NGO’s or social media. The attitudes of citizens towards sustainable behaviour should be well understood when promoting sustainable consumption or organic food. The present study is one of the first to investigate the impact of lifestyle on environmental factors in a French context, thus breaking the ground for more studies in the future, in France and European contexts. We would also argue that while climate change adaptation should be encouraged, there is a need for research to identify what should sustainable consumption mode be in the years to come. In addition, further analysis might be extended into some other areas such as economic and social factors and their role in the protection of the environment can also be explored in future research. Given the urgent need to achieve rapid transformation of consumption, some extensions of our work should include the investigation of additional variables for social and economic conditions affecting French sustainable behaviour. Are some economic or social conditions more likely to promote sustainable behaviour, and are these conditions dependent on and reinforced by the salience of the specific goods and practices? For scholars, a clearer understanding of the presence of sustainable practices could provide a better appreciation of the economic and social conditions related to their emergence and a better alignment between national and regional policy tools in this field. Considering the ongoing discussion on sustainability and sustainable behaviour, it is crucial for future studies to analyse the roles of institutional factors in order to identify the societal drivers and barriers to enforce the implementation of more sustainability engagement among citizens in various countries.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Sylvain Mbongui-Kialo

Notes

References

Andersson

Nässén

Larsson

, et al. (2014) Greenhouse gas emissions and subjective well-being: an analysis of Swedish households. Ecological Economics 102: 75–82.

Ashmore

Christie

Tyler

(2017) Symbolic transport choice across national cultures: theoretical considerations for research design. Transportation Planning and Technology 40(8): 875–900. DOI: 10.1080/03081060.2017.1355882.

Auffhammer

(2022) Climate Adaptive Response Estimation: short and long run impacts of climate change on residential electricity and natural gas consumption. Journal of Environmental Economics and Management 114: 102669.

Bai

Liu

(2013) An exploration of residents’ low-carbon awareness and behavior in Tianjin, China. Energy Policy 61: 1261–1270.

Baiocchi

Minx

Hubacek

(2010) The impact of social factors and consumer behavior on carbon dioxide emissions in the United Kingdom: a regression based on Input−Output and geodemographic consumer segmentation data. Journal of Industrial Ecology 14(1): 50–72.

Biesiot

Noorman

(1999) Energy requirements of household consumption: a case study of The Netherlands. Ecological Economics 28(3): 367–383.

Bin

Dowlatabadi

(2005) Consumer lifestyle approach to US energy use and the related CO2 emissions. Energy Policy 33: 197–208.

Bøhlerengen

Wiium

(2022) Environmental attitudes, behaviours, and responsibility perceptions among Norwegian youth: associations with positive youth development indicators. Frontiers in Psychology 13: 1664–1678.

Bohringer

Rosendahl

(2022) Europe beyond coal – an economic and climate impact assessment. Journal of Environmental Economics and Management 113: 102658.

10.

Brobakk

(2017) Klima for endring ? Norske bønders holdninger til klimaendring og klimapolitikk. Norsk Statsvitenskapelig Tidsskrift 333/4: 272–291.

11.

Büchs

Schnepf

(2013) Who emits most? Associations between socio-economic factors and UK households' home energy, transport, indirect and total CO2 emissions. Ecological Economics 90: 114–123.

12.

Buttel

(1987) New directions in environmental sociology. Annual Review of Sociology 13: 465–488.

13.

Carter

(2011) Recognizing the role of positive emotions in fostering environmentally responsible behaviors. Ecopsychology 3: 65–69.

14.

De Boer

de Witt

Aiking

(2016) Help the climate, change your diet: a cross-sectional study on how to involve consumers in a transition to a low-carbon society. Appetite 98: 19–27. DOI: 10.1016/j.appet.2015.12.001.

15.

Druckman

Jackson

(2009) The carbon footprint of UK households 1990–2004: a socio-economically disaggregated, quasi-multi-regional input-output model. Ecological Economics 68(7): 2066–2077.

16.

Duchin

(1998) Structural Economics: Measuring Changes in Technology, Lifestyles and the Environment. Washington, DC: Island Press.

17.

Dunlap

Catton

(1994) Struggling with human exemptionalism: the rise, decline and revitalization of environmental sociology. The American Sociologist 25(1): 5–30.

18.

Fazey

Proust

Newell

BJA

, et al. (2006) Eliciting the implicit knowledge and perceptions of on-ground conservation managers of the Macquarie Marshes. Ecology and Society 11(1): 25.

19.

Gadenne

Sharma

Kerr

, et al. (2011) The influence of consumers' environmental beliefs and attitudes on energy saving behaviours. Energy Policy 39(12): 7684–7694.

20.

Geels

(2005) Processes and patterns in transitions and system innovations: refining the co-evolutionary multi-level perspective. Technological Forecasting and Social Change 72(6): 681–696.

21.

Gersl

Hermanek

(2007) Financial Stability Indicators: Advantages and Disadvantages of Their Use in the Assessment of Financial System Stability. Prague: Czech National Bank. Occasional Publications-Chapters in Edited Volumes, 69–79.

22.

Gjerstad

Fløttum

(2021) Climate change lifestyle narratives among Norwegian citizens: a linguistic analysis of survey discourse. European Policy Analysis 7(2): 386–404.

23.

Gkargkavouzi

Halkos

Matsiori

(2019) A Multi-dimensional Measure of Environmental Behavior: Exploring the Predictive Power of Connectedness to Nature, Ecological Worldview and Environmental Concern. Social Indicators Research 143(1): 859–879. DOI: 10.1007/s11205-018-1999-8.

24.

Gorges

Holz-Rau

(2021) Transition of mobility in companies – a semi-systematic literature review and bibliographic analysis on corporate mobility and its management. Transportation Research Interdisciplinary Perspectives 11: 100462.

25.

Holian

Kahn

(2015) Household carbon emissions from driving and center city quality of life. Ecological Economics 116: 362–368. DOI: 10.1016/j.jeem.2022.102658.

26.

Jolliffe

(2002) Principal Component Analysis for Special Types of Data. New York: Springer, 338–372.

27.

Jolliffe

Cadima

(2016) Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences 374(2065): 20150202. DOI: 10.1098/rsta.2015.0202.

28.

Kaida

(2016) Pro-environmental behaviour correlates with present and future subjective well-being. Environment, Development and Sustainability 18(1): 1–17.

29.

Kerkhof

Benders

RMJ

Moll

(2009) Determinants of variation in household CO2, emissions between and within countries. Energy Policy 37(4): 1509–1517.

30.

Lazaric

Le Guel

Belin

, et al. (2020) Determinants of sustainable consumption in France: the importance of social influence and environmental values. Journal of Evolutionary Economics 30: 1337–1366.

31.

Zhang

(2019) The impact of social awareness and lifestyles on household carbon emissions in China. Ecological Economics 160(2): 145–155.

32.

Maloney

Ward

(1973) Ecology: let's hear from the people: an objective scale for the measurement of ecological attitudes and knowledge. American Psychologist 28(7): 583–586.

33.

Menzel

(2013) Are emotions to blame? - the impact of non-analytical information processing on decision-making and implications for fostering sustainability. Ecological Economics 96: 71–78.

34.

Nelson

Perli

(2007) Selected indicators of financial stability. Risk Measurement and Systemic Risk 4: 343–372.

35.

Nicholson

Snyder

(2011) Microeconomic Theory: Basic Principles and Extensions. Toronto: Nelson Education.

36.

Olsson

Folke

(2001) Local ecological knowledge and institutional dynamics for ecosystem management: a study of lake racken watershed, Sweden. Ecosystems 4: 85–104. DOI: 10.1007/s100210000061.

37.

Paddock

(2017) Household consumption and environmental change: rethinking the policy problem through narratives of food practice. Journal of Consumer Culture 17(1): 122–139.

38.

Pearce

Brown

Nerlich

, et al. (2015) Communicating climate change : conduits, content, and consensus. WIREs Climate Change 6(6): 613–626.

39.

Petev

Demoli

Sven

, et al. (2017) Styles de vie et Environnement. Fondation Nationale des Sciences Politiques (FNSP). Paris: Centre de données Socio-Politiques (CDSP).

40.

Pojani

Stead

(2017) Sustainable Urban Transport in the Developing World: Beyond Megacities. Sustainability 7: 7784–7805. DOI: 10.3390/su7067784.

41.

Pojani

Stead

(2018) Policy design for sustainable urban transport in the global south. Policy Design and Practice 1(2): 90–102.

42.

Poortinga

Steg

Vlek

Poortinga

(2004) Values, environmental concern, and environmental behavior - A study into household energy use. Environment and Behavior 36(1).

43.

Zeng

, et al. (2013) Household carbon dioxide emissions from peasants and herdsmen in northwestern arid-alpine regions, China. China. Energy Policy 57(6): 133–140.

44.

Raymond

Fazey

Reed

, et al. (2010) Integrating local and scientific knowledge for environmental management. Journal of Environmental Management 91(8): 1766–1777.

45.

Rees

(1995) Reducing the ecological footprint of consumption. The Workshop on Policy Measures for Changing Consumption Patterns. Seoul, South Korea: www.earthsummit2002.org/roadmap/consumpt.htm.

46.

Reid

Berkes

Wilbanks , et al. (2006) Bridging Scales and Knowledge Systems: Concepts and Applications in Ecosystem Assessment. Millennium Ecosystem Assessment and. Washington, DC: Island Press.

47.

Rouabah

(2007) Mesure de la vulnérabilité du secteur bancaire luxembourgeois. Luxembourg: Central Bank of Luxembourg.

48.

Saari

Damberg

Frömbling

, et al. (2021) Sustainable consumption behavior of Europeans: the influence of environmental knowledge and risk perception on environmental concern and behavioral intention. Ecological Economics 189: 107155. DOI: 10.1016/j.ecolecon.2021.107155.

49.

Schipper

Bartlett

Hawk

, et al. (1989) Linking life-styles and energy use: a matter of time? Annual Review of Energy 14: 273–320.

50.

Şimşekoğlu

Nordfjærn

Rundmo

(2015) The role of attitudes, transport priorities, and car use habit for travel mode use and intentions to use public transportation in an urban Norwegian public. Transport Policy 42: 113–120. DOI: 10.1016/j.tranpol.2015.05.019.

51.

Ang

(2017) Multiplicative structural decomposition analysis of aggregate embodied energy and emission intensities. Energy Economics 65: 137–147.

52.

Sundblad

E-L

Biel

Gärling

(2014) Intention to change activities that reduce carbon dioxide emissions related to worry about global climate change consequences. European Review of Applied Psychology 64(1): 13–17. DOI: 10.1016/j.erap.2011.12.001.

53.

The Intergovernmental Panel on Climate Change (IPCC) (2021) Climate Change Synthesis Report 2021. Geneva: Intergovernmental Panel on Climate Change.

54.

Tilbury

(1995) Environmental education for sustainability: defining the new focus of environmental education in the 1990s. Environmental Education Research 1(2): 195–212.

55.

Tran

(2006) Public perception of development issues: public awareness can contribute to sustainable development of a small island. Ocean & Coastal Management 49(5): 367–383.

56.

Tvinnereim

Flottum

Gjerstad

, et al. (2017) Citizens’ preferences for tackling climate change. Quantitative and qualitative analyses of their freely formulated solutions. Global Environmental Change 46: 34–41.

57.

Underwood

Zahran

(2015) The carbon implications of declining household scale economies. Ecological Economics 116: 182–190.

58.

Von Borgstede

Andersson

Johnsson

(2013) Public attitudes to climate change and carbon mitigation – implications for energy-associated behaviours. Energy Policy 57: 182–193.

59.

Wei

Liu

Fan

, et al. (2007) The impact of lifestyle on energy use and CO2 emission: an empirical analysis of China's residents. Energy Policy 35(1): 247–257.

60.

Whitmarsh

Seyfang

O’Neill

(2011) Public engagement with carbon and climate change: to what extent is the public ‘carbon capable’? Global Environmental Change 21: 56–65.

61.

Wilson

Tyedmers

Spinney

(2013) An exploration of the relationship between socioeconomic and well-being variables and household greenhouse gas emissions. Journal of Industrial Ecology 17(6): 880–891.

62.

World Bank (2007) Growth and CO2 Emissions: How Do Different Countries Fare? Washington, DC: Environment Department.

63.

Han

(2016) Household carbon inequality in urban China, its sources and determinants. Ecological Economics 128: 77–86.

64.

Yahoo

Othman

(2017) Employing a CGE model in analysing the environmental and economy-wide impacts of CO 2 emission abatement policies in Malaysia. The Science of the Total Environment 584–585: 234–243.

65.

Yang

Fan

Zheng

(2016) Determinants of household carbon emissions: pathway toward eco-community in Beijing. Habitat International 57: 175–186.