Assessing and prospecting decoupling effect of carbon emissions from economic growth: Empirical studies from Chinese provinces

Abstract

China has proposed ambitious goals of carbon peak and carbon neutrality, and will pay more attention to the relationship between economic development and carbon emissions. It's significant to assess the current environmental decoupling and prospect the future conditions in China. This article studies the decoupling status and influencing factors in Chinese provinces from 1996 to 2018 through Tapio decoupling index and decomposition model. The results show that most provinces are currently in a weak decoupling state. The growth of per capita GDP and population will affect the process of strong decoupling, while the optimization of energy intensity, energy structure and industrial structure will promote the realization of strong decoupling. Moreover, this paper prospects the decoupling scenarios of Chinese provinces from 2019 to 2035 and finds that all provinces will achieve strong decoupling before 2040, 2035 and 2030 under different carbon emissions scenarios respectively. To achieve the strong decoupling of economic output and carbon emissions as soon as possible, the government must improve energy efficiency, use renewable energy in large quantities, and promote the industrial transformation and upgrading.

Keywords

Economic growth carbon emissions decoupling analysis carbon peak

Introduction

Since the industrial revolution, human have used coal, oil and other fossil energy resources to develop economy, which causes more and more serious global climate problems, frequent natural disasters and extreme weather.¹ Under the promotion of the United Nations and other international organizations, major countries around the world have signed the Paris Climate Agreement which announced that the global average temperature will be controlled within 2°C compared with the pre industrial period, and would make efforts to limit the temperature rise less than 1.5 °C.² According to the actual conditions of each country, emissions reduction targets have been set to mitigate the greenhouse effect and achieve the decoupling between economy and carbon emissions as soon as possible.^3–5 The government in China mentioned that China would try to reach carbon peak in 2030 and strive to achieve carbon neutrality in 2060.⁶ In addition, the Fourteenth Five-year Plan also proposed that it should achieve fundamental improvement in ecological with reduction in carbon emissions and promote the development of green low-carbon economy. Besides, in order to achieve the low-carbon economy and the complete decoupling between carbon emissions and economy, China has established the national carbon market to realize energy transformation through the dual means combining policy and market.^7,8 Therefore, based on the above situation, more and more scholars are conducting research on the decoupling relationship between economy and carbon emissions. The research objects are mainly divided into the following three categories as shown in Table 1:

Most scholars choose multiple countries as the analysis object to study the decoupling relationship, and focus on environmental issues at the national level. Shuai et al.⁹ selected 133 countries as the research object to study the decoupling relationship among carbon intensity, total per capita carbon emissions, total carbon emissions and economic development. Then, a three-step strategy for future decoupling is proposed; Wu et al.¹⁰ and Wang and Su¹¹ also analyzed the current decoupling status of many countries; Some scholars;^12–15 Zhang et al.¹⁶ not only study the current situation of decoupling relationship, but also use decomposition models and environment Kuznets curve to determine the factors affecting decoupling. They put forward a series of policy recommendations to promote the decoupling between economic development and carbon emissions in the future, which is conducive to promoting the harmonious development of the economy and the environment.

Table 1.

Summary of the decoupling relationships between carbon emissions and economic growth.

Author	Country (s) & period	Variable	Method
National level
Akizu-Gardoki et al.¹⁷	126 countries(2000–2014)	E/Y	GMRIO/DI
Moutinho et al.¹²	Latin America countries(1994–2013)	C/Y	DEA/DI
Wu et al¹⁰.	Typical developed and developing countries(1965–2015)	C/Y	DI
Pao and Chen¹³	G20(1991–2016)	C/E/Y	EKC
Sanye-Mengual et al.¹⁸	European Union(2005–2014)	EI/Y	DI
Shuai et al.⁹	133 countries(2000–2014)	C/Y	DI
Wang and Wang¹⁹	the United States（1997–2015）	C/Y	Kaya/DI
Hu et al.¹⁴	the Belt and Road countries(1991–2016)	C/Y	DI/LMDI /Kaya
Wang and Su¹¹	192 countries(2000–2014)	C/Y	DI
Wang and Zhang¹⁵	BRICS countries(1996–2014)	C/Y	EKC/DI
Zhang et al. ¹⁶	China and the ASEAN countries(1990–2014)	C/Y	LMDI/DI
Regional level
Zhang and Bai²⁰	Shandong province(1995–2013)	E/Y	LMDI/DI
Chen et al. ²¹	Southwest China(2001–2015)	C/HDI	LMDI/DI
Shi²²	Shandong province(1997–2017)	C/Y	EKC/DI
Luo et al.²³	Central Plains Urban Agglomeration in China(2004–2015)	E/Y	DI
Li et al.²⁴	Yangtze River Economic Belt cities (2005–2017)	C/Y	DI
Zhang et al.²⁵	Northwest arid region in China(1997–2017)	E/Y	DI
Industry level
Ma and Cai²⁶	Tertiary Industry in China (2001–2015)	C/Y	LMDI/DI/EKC
Xie et al.²⁷	Electric power industry in China (1985–2016）	C/Y	LMDI/DI
Chen et al.²⁸	Global agricultural industry(2000–2016)_	E/Y	LMDI/DI
Raza and Lin²⁹	Transport sector in Pakistan（1984–2018）	C/Y	LMDI/DI
Wang et al.³⁰	Iron and steel industry in China (2001–2016)	C/Y	DI/GVM
Yu et al.³¹	Civil aviation sector in China (1979–2015)	C/Y	DI/LMDI /Kaya
Adha et al.³²	Residential electricity industry in Indonesia (2002–2018)	E/Y/P	TSA
Lin and Raza³³	Electric power industry in Pakistan (1989–2018）	E/Y	IDA/DI
Shiau et al.³⁴	Electric power industry in Taiwan (1998–2019)	E/Y	ANN

Note: Y, C, E,P, HDI and EI indicate GDP, carbon emissions, energy consumption, urban population, human development index and energy intensity. GMRIO represents global multi regional input-output; DI represents decoupling index; DEA represents data envelopment analysis; EKC represents environment Kuznets curve; Kaya represents Kaya Identity; LMDI represents logarithmic mean disivia index; GVM represents grey verhulst model; TSA represents two-stage analysis; ANN represents artificial neural network.

Additionally, some scholars tend to assess the decoupling relationships at regional level. Luo et al.²³ selected China's central plains urban agglomeration as the research object which is highly dependent on resources and studied its decoupling status from 2004 to 2015. The results show that the increasing in environmental decoupling promotes comprehensive decoupling. Zhang et al.²⁵ chose northwest arid region in China as the research object. Studying on the decoupling relationship between water resources utilization and economic development from 1997 to 2017, and found that economic development's dependence on water resources is gradually decreasing. Besides, some scholars used individual regions and provinces as the research objects to analyze the decoupling relationship between economic growth and carbon emissions of various industries in details, and put forward effective suggestions for the decoupling of the economy and environment.^20,22,35

Moreover, other studies on decoupling relationships laying particular emphasis on the industry level, especially the energy and transportation industry. Xie et al.²⁷ and Lin and Raza³³ respectively studied the decoupling relationship of the power industry in China and Pakistan, and both found that with the improvement of energy structure and energy efficiency, the decoupling index of power industry gradually declined. Although environmental pressure still exists, the economy will be completely decoupled from carbon emissions in the future. Yu et al.³¹ innovatively selected China's aviation industry as research object and combined the Tapio decoupling model with the LMDI decomposition model to analyze the decoupling scenarios from 1979 to 2015. At the same time, build a multi-scenario forecasting model to predict the total carbon emissions of the civil aviation industry from 2016 to 2049. It is pointed out that China's civil aviation industry will achieve a complete decoupling of economic development and carbon emissions after 2050. There are also a small number of scholars who analyze the decoupling scenarios of agriculture and service industries, and put forward policy recommendations on how to reduce the dependence of economic development on environmental resources from multiple perspectives.^26,³⁶

China's economy has developed rapidly in recent years. Economic development has caused serious consumption of resources. Energy consumption and carbon emissions of per unit GDP are still high. This means that current economic development in China is still dependent on resources and environment, and the complete decoupling between the economy and carbon emissions has not yet been achieved. With the proposal of carbon peak and carbon neutrality in China, the future coordinated development of economy and carbon emissions deserves attention. It is very important to identify the period for achieving low-carbon economy in each province. However, many scholars mainly concentrate on analyzing the relationships between economy and carbon emissions at present, thereby ignoring the progress of the relationships in the future.^16,^24,37,39^,53

Hence, to cover the research gap, this article has some improvements and contributions. Firstly, an extended STIRPAT model and a ridge regression model are constructed to predict the future provincial decoupling scenarios based on the conclusions of the LMDI model. In addition, this article set three predicting scenarios including low-speed carbon, base-speed carbon and high-speed carbon situations, and compare the decoupling relationships between economic growth and carbon emissions of each province from 2019–2035. Moreover, identify the time for 30 provinces to achieve strong decoupling, and provide policy suggestions for carbon neutrality and carbon peak.

The rest of the paper is structured as follows. Section 2 introduces the data source and methods. Section 3.1 uses the Tapio decoupling index to study the decoupling scenarios of 30 Chinese provinces from 1996 to 2018. Section 3.2 analyzes the factors affecting decoupling based on the LMDI decomposition model. Section 3.3 applies the extended STIRPAT model and ridge regression to predict the decoupling scenarios of 30 Chinese provinces from 2019 to 2035. Section 4 is the conclusion and related policy recommendations.

Data and method

Data source

This article uses the data of 30 provinces (except for Tibet, Hong Kong, Macao and Taiwan) in China from 1996 to 2018 for research. Among them, the data of economic output, total energy consumption and total population all come from the statistical yearbooks of each province and China energy statistical yearbooks¹. The total carbon emissions are obtained according to the calculation method proposed by IPCC²:

\begin{aligned} C = & \sum_{j = 1} E_{j} \times C E F \\ = & \sum_{j = 1} E_{j} \times N C V_{j} \times C C_{j} \times C O F \times α_{j} \end{aligned}

(1)

where, C represents carbon emissions; E represents energy consumption; CEF represents carbon emissions coefficient; NCV represents low-level energy emissions; CC represents carbon content per calorific value; COF represents carbon oxidation factor; α represents standard coal conversion coefficient; j represents energy type (as shown in Appendix B).

Tapio decoupling model

The decoupling theory was first proposed by the Organization for Economic Cooperation and Development (OECD). At the beginning of the 21st century, Tapio proposed the Tapio decoupling model based on the decoupling theory,⁴⁰ which is mainly used to evaluate the degree of interdependence between economic growth and environment, measuring the ability of low-carbon development for a region.^41,42 The specific formula is as follows:

T (Y, C) = \frac{Δ Y / Y}{Δ C / C} = \frac{(Y_{i + 1} - Y_{i}) / Y_{i}}{(C_{i + 1} - C_{i}) / C_{i}}

(2)

where, T (Y, C) represents the decoupling elasticity index between economic output and carbon emissions in year i.

As shown in Figure 1, based on the different decoupling elasticity indexes, the decoupling scenarios can be divided into eight types. Strong negative decoupling represents the scenario that at the same time of economic recession, resource consumption is increasing and environmental pollution is becoming more and more serious, which is the worst state. Strong decoupling represents the scenario that at the same time of economic growth, resource utilization rate is rising and carbon emissions are decreasing, which is the best state. The other decoupling scenarios are the transitional state from strong negative decoupling to strong decoupling.

Figure 1.

Classification of decoupling scenarios.

LMDI decomposition model

Compare with other decomposition methods, LMDI is easier to construct and is suitable for the model that contains multiple factors. Therefore, it is a commonly used exponential decomposition method.⁴³ In order to further explore the decoupling relationship between carbon emissions and economy, this paper decomposes the carbon emissions of each province by using the LMDI model, which can analyze the factors that affect the decoupling situation. The formulas are as follows:

\begin{aligned} C = & \sum_{n} \sum_{i} \sum_{l} \sum_{j} \frac{C_{n i l j}}{E_{n i l j}} \times \frac{E_{n i l j}}{E_{n i l}} \times \frac{E_{n i l}}{Y_{n i l}} \times \frac{Y_{n i l}}{Y_{n i}} \times \frac{Y_{n i}}{P_{n i}} \times P_{n i} \\ = & \sum_{n} \sum_{i} \sum_{l} \sum_{j} C I_{n i l j} \times E S_{n i l j} \times E I_{n i l} \times Y S_{n i l} \times Y P_{n i} \times P_{n i} \end{aligned}

(3)

where, n represents the provinces of China ; i represents the number of year; l represents the industry; j represents the energy type; C represents carbon emissions; E represents energy consumption; Y represents provincial economic output; P represents the total population; CI represents the carbon emissions coefficient of energy; ES represents energy structure; EI represents energy intensity; YS represents industrial structure; YP represents per capita gross domestic product.

Δ C = C^{t_{1}} - C^{t_{0}} = Δ C_{C I} + Δ C_{E S} + Δ C_{E I} + Δ C_{Y S} + Δ C_{Y P} + Δ C_{P}

(4)

Δ C_{C I} = \sum_{n} \sum_{i} \sum_{l} \sum_{j} L (C_{n i l j}^{t_{1}}, C_{n i l j}^{t_{0}}) I n (\frac{C I_{n i l j}^{t_{1}}}{C I_{n i l j}^{t_{0}}})

(5)

Δ C_{E S} = \sum_{n} \sum_{i} \sum_{l} \sum_{j} L (C_{n i l j}^{t_{1}}, C_{n i l j}^{t_{0}}) I n (\frac{E S_{n i l j}^{t_{1}}}{E S_{n i l j}^{t_{0}}})

(6)

Δ C_{E I} = \sum_{n} \sum_{i} \sum_{l} \sum_{j} L (C_{n i l j}^{t_{1}}, C_{n i l j}^{t_{0}}) I n (\frac{E I_{n i l}^{t_{1}}}{E I_{n i l}^{t_{0}}})

(7)

Δ C_{Y S} = \sum_{n} \sum_{i} \sum_{l} \sum_{j} L (C_{n i l j}^{t_{1}}, C_{n i l j}^{t_{0}}) I n (\frac{Y S_{n i l}^{t_{1}}}{Y S_{n i l}^{t_{0}}})

(8)

Δ C_{Y P} = \sum_{n} \sum_{i} \sum_{l} \sum_{j} L (C_{n i l j}^{t_{1}}, C_{n i l j}^{t_{0}}) I n (\frac{Y P_{n i}^{t_{1}}}{Y P_{n i}^{t_{0}}})

(9)

Δ C_{P} = \sum_{n} \sum_{i} \sum_{l} \sum_{j} L (C_{n i l j}^{t_{1}}, C_{n i l j}^{t_{0}}) I n (\frac{P_{n i}^{t_{1}}}{P_{n i}^{t_{0}}})

(10)

L (C_{n i l j}^{t_{1}}, C_{n i l j}^{t_{0}}) = {\begin{matrix} \frac{C_{n i l j}^{t_{1}} - C_{n i l j}^{t_{0}}}{I n C_{n i l j}^{t_{1}} - I n C_{n i l j}^{t_{0}}} f o r C_{n i l j}^{t_{1}} \neq C_{n i l j}^{t_{0}} \\ C_{n i l j}^{t_{1}} f o r C_{n i l j}^{t_{1}} = C_{n i l j}^{t_{0}} \end{matrix}

(11)

Extended STIRPAT model and ridge regression

IPAT model is a classical model for environmental pressure decomposition, which is used to study the impact of economy and social factors on the environment. However, the regression residual value of the model is a known value, which cannot guarantee the randomness of hypothesis testing and has certain limitations. Therefore, Dietz and Rosa⁴⁴ improved IPAT model and proposed STIRPAT model. Now more and more scholars use STIRPAT model to predict the environmental and ecological conditions in the future. Based on the results of LMDI decomposition model, an extended STIRPAT model including per capita GDP, energy structure, energy intensity, industrial structure and population is established, which is used to predict future decoupling scenarios in this paper, the formulas are as follows:

C_{i n} = γ E S_{i n}^{b} E I_{i n}^{c} Y S_{i n}^{d} Y P_{i n}^{e} (Y P_{i n}^{2})^{f} P_{i n}^{g} τ_{i n}

(12)

\ln (C_{i n}) = a + b \ln (E S_{i n}) + c \ln (E I_{i n}) + d \ln (Y S_{i n}) + e \ln (Y P_{i n}) + f \ln (Y P_{i n})^{2} + g \ln (P_{i n}) + ε_{i n}

(13)

where b, c, d, e, f and g represent the coefficients of ES, EI, YS, YP, YP² and P respectively, and

a, τ, ε, γ

represents the constant term.

When the model is used for prediction, the variance expansion factor is prone to be too large. The multicollinearity problem is obvious and the conventional least square method cannot be used. Therefore, this paper uses ridge regression proposed by Hoerl and Kennard⁴⁵ instead of OSL to eliminate the collinearity problem and make predictions. Its objective function is as follows:

β^{r i d g e} = a r g m i n (‖ Y - X β ‖^{2} + k ‖ β ‖^{2})

(14)

Estimated value:

{\hat{β}}^{r i d g e} = a r g m i n ({\sum_{i = 1}^{n} (y_{i} - β_{0} - \sum_{j = 1}^{p} β_{j} x_{i j})}^{2} + k \sum_{j = 1}^{p} β_{j}^{2})

(15)

Residual sum of squares:

R S S (k) = (Y - X β)^{T} (Y - X β) + k β^{T} β

(16)

The partial derivative of β:

{\hat{β}}^{r i d g e} (k) = (X^{T} X + k I)^{- 1} X^{T} Y

(17)

where I is the identity matrix;

{\hat{β}}^{r i d g e} (k)

is the final solution of ridge regression; K is the ridge parameter. When k = 0, ridge regression is transformed into OLS model.

Results and discussion

Provincial decoupling relationship in China

Based on the Tapio decoupling model, this article summarizes the decoupling relationship between economic output and carbon emissions of China's provinces spanning the period 1996–2018. In order to analyze the decoupling situation more conveniently and scientifically in recent years, we rank each province based on the value of carbon intensity index which measures the relationship between carbon emissions and economic output. The results are shown in Figure 2.

Figure 2.

1996-2018 China's provincial decoupling relationship. Note: The ranking of provinces is based on the value of carbon intensity. Carbon emissions intensity is equal to total carbon emissions divided by total economic output.

On the whole, most provinces have experienced the decoupling situation from expansive negative decoupling, expansive coupling to weak decoupling. At the same time, the decoupling index is also declining. Additionally, provinces with lower carbon intensity have better decoupling status and lower decoupling index; provinces with higher carbon intensity have poor decoupling status and higher decoupling index. It shows that there is a certain connection between carbon intensity and the decoupling relationship, which proves the reliability and rationality of using carbon intensity to rank the decoupling state of provinces.

From the time perspective, we divide the time period into four stages according to the law of decoupling relationships:1996–2000;2001–2005;2006–2010;2011–2018.

In the first time period, there are multiple decoupling scenarios at this stage, which mean that the relationship between economic output and carbon emissions is unstable. As shown in Figure 3, 1996–2000 is the Ninth Five-Year Plan which is the first long-term plan after the development of the socialist market economy, and also a cross-century development plan in China. The economic development of various provinces is not balanced yet.

Figure 3.

Relevant economic and energy policies in China.

In the second time period, for most provinces, weak decoupling, expansive coupling and expansive negative decoupling occurred alternately. The three states all indicate that economic output and carbon emissions continue increasing but the growth rate of total carbon emissions is higher than economic output. As shown in Figure 3, this is due to the fact that China joined the World Trade Organization in 2001 and the process of industrialization was continuously promoted. In 2003–2005, China's GDP growth rate reached more than 10%. High-speed economic growth will inevitably bring about serious consumption of resources and massive emissions of pollutants.

In the third time period, most provinces are dominated by weak decoupling and expansive coupling. Although the growth rate of carbon emissions is still higher than the growth rate of GDP, the decoupling index continues to decline. These conclusions are same with Dong et al.⁴⁶ As shown in Figure 3, this is because during the “Eleventh Five-Year” period, energy-saving and emissions reduction policies were proposed. The goal is to reduce energy consumption per unit of GDP by about 20% and the total discharge of major pollutants by about 10% during the “Eleventh Five-Year” period.⁴⁷ The growth rate of carbon emissions has been declining gradually and the industrial structure has begun to transform.

In the fourth time period, most provinces are dominated by weak decoupling and the decoupling index has been declining, which is similar with study of Wu et al.³⁹ In addition, some provinces have obvious strong decoupling status. The decoupling index of most provinces has dropped to about 0.3. As shown in Figure 3, this is because during this period, the state revised the “Renewable Energy Law” and proposed a renewable energy power generation quota system. At the same time, China began to establish pilot carbon markets to curb carbon emissions by limiting free quotas in key industries. Through the dual effectiveness of markets and policies, the proportion of renewable energy in energy consumption has gradually increased, and the total carbon emissions have been significantly reduced.^48,49

From the regional perspective, during the fourth time period (2010–2018), Beijing, Shanghai and Guangzhou have basically achieved strong decoupling. Some economically developed provinces such as Jiangsu, Zhejiang, Tianjin, Fujian and Hubei have experienced temporary strong decoupling. It is obvious that the distribution of strong decoupling is gradually concentrated in these provinces. This is because the pilot markets in China have begun to implement in these provinces, which will conduce to guide technology and funds to low-carbon areas and promote the development of green economy. In addition, the central and southern underdeveloped provinces such as Anhui and Chongqing have always been in the weak decoupling state. However, in the developing western provinces and the provinces with a high degree of carbon dependence, such as Gansu, Qinghai, and Shanxi, weak negative decoupling and expansive negative decoupling still appear. This is because high energy consumption and pollution industries in China are mostly concentrated in the western provinces and provinces with sufficient resources. These provinces have contributed production capacity to the developed provinces, which cause severe environmental damage in the process of economic development.

Decomposition analysis of decoupling relationship

This paper takes five years as a period to divide the carbon emissions data of 30 provinces into four time periods: 1996–2000, 2001–2005, 2006–2010, and 2011–2018 to analyzes the contribution of carbon emissions coefficient of energy, energy structure, energy intensity, industrial structure, per capita GDP and population to carbon emissions, which could reflect the influence of various factors on the decoupling relationship. The results are shown in the Figure 4.

Figure 4.

Decomposition results of decoupling relationship in 30 provinces.

From a general perspective, the impact of per capita GDP and population on carbon emissions is always positive. Compared with population, per capita GDP has a greater contribution to the growth of total carbon emissions. This is because China's per capita GDP has grown rapidly in recent years. The per capita GDP growth rate reached 1286%, while the growth rate of the total population was only 15.9%. The impact of energy intensity on carbon emissions is always negative. Energy intensity is an important factor in energy saving and emissions reduction. Further reduction of energy intensity will promote the realization of carbon peaking and carbon neutrality. Additionally, for most provinces, the energy structure has a negative impact on carbon emissions. The impact of industrial structure on carbon emissions varies in different periods and different provinces. These conclusions are partly similar with Boqiang and Wang⁵⁰ and Wang et al.⁵¹

From the time perspective, the contribution rate of per capita GDP, population, energy intensity and energy structure did not change much from 1996 to 2018, and the contribution rate of per capita GDP has been maintained at about 50%. It is worth noting that the impact of industrial structure on carbon emissions was sometimes positive and sometimes negative before 2010, while the impact of the industrial structure has been negative after 2010. This is because the industrial structure of various provinces has been continuously optimized from 2010 to 2018. The proportion of tertiary industry in more than 70% of provinces has surpassed that of secondary industry. The focus of economic development is gradually shifting from the energy industry to the low-carbon industry.²⁵

From the regional perspective, as shown in Figure 5, the effects of per capita GDP and population on carbon emissions in 30 provinces are both positive and the effect of energy intensity on carbon emissions in 30 provinces is negative. The impact of industrial structure on carbon emissions is negative in the southeast coastal areas such as Zhejiang, Shanghai, Jiangsu and Guangdong and is positive in the northwest inland areas such as Ningxia, Qinghai, Xinjiang and Gansu. Additionally, the influence of the industrial structure has changed from positive to negative over time in the central and eastern regions such as Hubei, Hunan, Liaoning, and Shandong. This is because the industrial structure of the southeast coastal areas is dominated by tertiary industries and high-tech industries, while the northwest inland area is still dominated by primary and secondary industries. And the central and eastern regions have undergone industrial restructuring.⁵²^,53

Figure 5.

The influence of various factors on the strong decoupling relationship. Note: “ + ” mans that this factor will promote the strong decoupling of economic output and carbon emissions:”-“means that this factor will inhibit the strong decoupling of economic output and carbon emissions.

The impact of energy structure on carbon emissions in Shanxi, Hebei, Inner Mongolia and etc. is positive. These provinces have large coal production and rely on resource endowments to develop their economy. While the impact of energy structure on carbon emissions in Sichuan, Guangdong, and Qinghai and etc. is negative. The development of renewable energy in these provinces is relatively better, and they rely on wind, light, and hydropower to boost their economy.

Prospection of decoupling relationship

Scenario setting analysis

After constructing an extended SPIRPAT model that includes carbon emissions, industrial structure, energy structure, energy intensity, GDP per capita, and population, this article sets three development scenarios: high-speed, base-speed and low-speed as shown in Appendix D, Appendix E and Appendix F to predict the decoupling scenarios in 2019–2025, 2026–2030 and 2031–2035.

As shown in Figure 6, the settings of the three scenarios are based on the relevant documents of the country, region and each province. According to the LMDI model and the research results of Yu et al.³¹ and Shi,²² it can be found that when the growth rate of total population and per capita GDP is high, and the decline rate of energy structure, industrial structure and energy intensity is low, the carbon emissions are at the high-speed growth mode; when the growth rate of total population and GDP per capita is moderate, and the decline rate of energy structure, industrial structure and energy intensity is moderate, the total carbon emissions are at the base-speed growth mode; when the growth rate of total population and GDP per capita is low, and the decline rate of energy structure, industrial structure and energy intensity is high, the total carbon emissions are at the low-speed growth mode.

Figure 6.

Setting process of three development scenarios. Note: The blue circle on the right picture represents the decline rate mode of energy structure, industrial structure and energy intensity. The yellow circle on the right picture represents the growth rate mode of total population and GDP per capita. L, M and H represent low, moderate and high.

Prediction of carbon emissions and strong decoupling time

Based on the scenario settings of each province, the ridge regression and the expanded STIRPAT model are used to predict the province's carbon emissions from 2019 to 2035. The results are shown in the Figure 7:

The carbon emissions prediction curves in green (Beijing and Shanghai) show a clear downward trend. This is due to the upgrading of the industrial and energy structure, the total carbon emissions have been continuously reduced. Such areas should continue to maintain their current development policies. The carbon emissions prediction curves in purple show a trend of first rising and then falling, which represents a negative growth in carbon emissions in the later period. This is because some of these provinces (such as Zhejiang, Jiangsu, Guangdong, Tianjin and etc.) are at the high level of economic development with reasonable industrial and energy structures. Others are low-reliance on high-carbon industries and their renewable energy is widely used (such as Hainan, Sichuan, Gansu, Inner Mongolia and etc.). These provinces need to continue to improve energy efficiency and promote the development of low-carbon industries. At the same time increase the proportion of renewable energy and strengthen the renewable energy technology. In some provinces (such as Hebei, Shanxi, Liaoning, Heilongjiang, etc.), the carbon emissions prediction curve in blue shows an upward trend, but the slope of the curve continues to decrease, which means that the growth rate of carbon emissions is gradually decreasing. Such provinces are highly dependent on the energy industry, and the energy structure and industrial structure are difficult to achieve complete transformation in the short term. Therefore, it is necessary to continue to strictly implement the policy of energy conservation and emissions reduction, and promote the replacement of non-renewable energy by renewable energy.

Due to the continuous promotion of energy saving and emissions reduction policies and the large-scale use of renewable energy, energy has become more low-carbon and cleaner. Therefore, the total amount of carbon emissions in 30 provinces will reach a peak before 2035. Beijing and Shanghai, which are economically developed and dominated by tertiary industries, will peak their carbon emissions before 2025; Provinces with rapid economic development and successful industrial transformation and upgrading (such as Tianjin, Jiangsu, Zhejiang, Anhui, Guangdong, Chongqing, etc.) will reach their carbon emissions peak between 2025 and 2030; And there are some provinces with slower economic development or dominated by energy industry (such as: Shanxi, Guizhou, Qinghai, Xinjiang, etc.) will peak their carbon emissions after 2030.

Figure 7.

Provincial prediction results of carbon emissions from 2019 to 2035. Note: Based on the different shapes of the carbon emissions prediction curve, they are divided into three categories: Green curve, purple curve and blue curve.

Compared with other literatures as shown in Figure 8, because forecasting methods and the influencing factors included in the model are different, the carbon peak time obtained in each literature is not similar. But, the peak time of carbon emissions in China is concentrated in 2025–2040.^36,54^,57 At the same time, because the degree of economic development and energy utilization are different, the carbon peak time of each region and industry is not same.^22,58^,59 These conclusions are similar with this article.

Based on the results of carbon emissions predictions and economy growth rate, the time for China's provinces to achieve strong decoupling is shown in the Figure 9–Figure 11. It can be seen obviously from the figures that provinces with large carbon emissions will reach their carbon peak relatively later.

Figure 8.

Prediction results of carbon peak in other articles.

Figure 9.

Time for the strong decoupling between economy and carbon emissions in high-speed carbon scenario.

Figure 10.

Time for the strong decoupling between economy and carbon emissions in base-speed carbon scenario.

Figure 11.

Time for the strong decoupling between economy and carbon emissions in low-speed carbon scenario.

Under the high-speed carbon scenario, most provinces will be completely decoupled from 2030–2035; provinces in the North China Plain and Northeast Plain will be strongly decoupled after 2035; coastal areas will be completely decoupled earlier than inland regions. Zhejiang, Jiangsu, Shanghai and etc. will achieve the strong decoupling of GDP and carbon emissions before 2030, which means that economic development will no longer be at the expense of energy consumption, and greenhouse gas emissions will gradually decrease.

Under the base-speed carbon scenario, besides a few provinces with rich coal resources and large populations such as Shanxi, Hebei, Henan and Shandong, other provinces can achieve complete decoupling of economic output and carbon emissions by 2035, that is, positive growth in economy accompanying by negative growth in carbon emissions in these provinces. Compared with the high-speed carbon scenario, Chongqing, Fujian and Guangdong can achieve strong decoupling by 2030. And Jiangsu, Zhejiang and Shanghai have also completed the strong decoupling ahead of schedule.

Under the low-speed carbon scenario, nearly half of provinces such as Guangdong, Hubei, Anhui and other provinces will achieve complete decoupling of economic output and carbon emissions by 2030, and strong decoupling will occur in other provinces before 2035. Compared with the base-speed carbon scenario, some central and western provinces, such as Yunnan, Sichuan, Guangxi and etc., will appear a turning point in carbon emissions and achieve strong decoupling before 2030.

Under the three scenarios, most provinces will reach their carbon emissions peak and achieve a strong decoupling between carbon emissions and the economy before 2035. These results show that the policy that “China will strive to reach the peak of carbon dioxide emissions by 2030” proposed by the nation cannot be achieved easily. Therefore, the government needs to adjust the current economic and energy policies. Because the factors affecting the strong decoupling relationships between carbon emissions and economy considered in other articles are inconsistent, the strong decoupling time will be earlier than 2030.^60,62

Conclusions and policy implications

Conclusions

In recent years, the continuous development of China's economy has also brought about a series of environmental problems. Greenhouse gas emissions continue to increase and the greenhouse effect becomes more and more serious. With the introduction of carbon peak and carbon neutrality targets, how to achieve the decoupling between economic development and carbon emissions is worthy of attention. Therefore, based on the Tapio decoupling index, LMDI decomposition method and STIRPAT model, this paper analyzes the current and future decoupling situation in China's provinces. The main conclusions are as follows:

It can be clearly found that the decoupling relationships among provinces in China have changed from the coexistence of multiple decoupling relationships in the 9th five-year plan to mainly weak decoupling relationships in the 13th five-year plan. The change of decoupling status can be divided into five years as a period, which is synchronized with China's five-year plan, indicating the effectiveness of the five-year plan. It can well guide the development of economy and energy in China.

The growth of per capita GDP and population will increase the amount of carbon emissions to some extent, that is, unscientific economic development and encouraging population growth will affect the efforts of emissions reduction. Furthermore, the growth of the energy structure, energy intensity, and industrial structure will inhibit carbon emissions to some extent, that is, the optimization of energy and industry is a useful means to realize strong decoupling as soon as possible.

In addition, according to the prospection results of the ridge regression and the STIRPAT model, it can be concluded that most provinces will experience negative growth in carbon emissions and achieve a strong decoupling of the economy and carbon emissions before 2035, which means it is still difficult to achieve carbon peak before 2030.

Policy implications

According to the conclusions of this study, there are some policy implications for decoupling relationships between economy and carbon emissions:

The five-year plan plays a guiding role in China's energy and economic development. It must be relied on to realize the comprehensive strong decoupling of China's provinces. Future plans should include more information on carbon emissions reduction and energy sustainable technology.

According to the conclusions of the factors affecting decoupling status, we can suggest that on the one hand, it is necessary to improve the quality of economic development rather than the speed; on the other hand, China should improve the dual control of total energy consumption and intensity and replace fossils with renewable energy such as photovoltaic and wind power, which will be the most critical path to achieve carbon neutrality goal. More importantly, with the proposal of the “Three-child policy”, it is unrealistic to limit population growth to curb carbon emissions. The government needs to strengthen public awareness of emissions reduction, and try to bring social groups into the carbon market to get compensation for emissions reduction in order to enhance its enthusiasm for protecting the environment.

For provinces to achieve strong decoupling, government must promptly formulate carbon emissions reduction action plan at the provincial level. Provinces that have achieved strong decoupling should continue to improve carbon emissions intensity, promote green and low-carbon circular development. Meanwhile, it is necessary to provide guidance and support for other provinces to achieve carbon peak; provinces that are close to strong decoupling should continue to optimize the industrial structure and energy structure, and promote key industries to reach carbon peak first; provinces that are difficult to achieve strong decoupling, not only require the transformation of industry and energy structure, but require inter-provincial cooperation to reduce carbon emissions. Additionally, relying on the national carbon market, these provinces could increase the carbon cost on the consumption side and avoid carbon leakage from developed provinces so as to accelerate the realization of carbon peak. In brief, China should continue to fight the battle of pollution prevention and realize the synergistic effect of pollution reduction and carbon reduction³.

Limitations of this study

Several limitations exist in this study. The actual situations of each province are different, therefore the affecting factors of decoupling relationships should be selected uniquely. In addition, this paper only predicts the provincial decoupling relationships up to 2035, however, it does not involve the decoupling scenarios from 2035–2060, which could not propose detailed suggestions about carbon neutrality. These investigations can be explored in the future.

Footnotes

Acknowledgements

The authors would like to thank the anonymous referees and the editor of this journal. The authors also gratefully acknowledge the financial support of the National Natural Science Foundation of China (Grant No.72074075), the Natural Science Foundation of Beijing (Grant No. 9212017) and the Fundamental Research Funds for the Central Universities (Grant No. 2019FR002).

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request

Competing interests

The authors declare that they have no competing interests.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the the National Natural Science Foundation of China, the Fundamental Research Funds for the Central Universities, the Natural Science Foundation of Beijing, (grant number 72074075, 2019FR002, 9212017).

ORCID iD

Xingping Zhang

Notes

Appendix

Appendix F.

30 provinces’ scenario setting in 2031-2035

		ES	EI	IS	YP	P			ES	EI	IS	YP	P			ES	EI	YS	YP	P			ES	EI	YS	YP	P
Beijing	H	−3.10%	−6.70%	−4.20%	6.30%	0.00%	Henan	H	−1.20%	−6.20%	−3.20%	6.10%	0.10%	Shanghai	H	−3.0%	−6.5%	−4.0%	6.9%	0.4%	Guizhou	H	−1.8%	−6.0%	−2.0%	8.0%	0.8%
	B	−3.70%	−7.20%	−4.70%	6.00%	−0.20%		B	−1.70%	−6.70%	−3.70%	5.80%	−0.10%		B	−3.5%	−7.0%	−4.5%	6.6%	0.2%		B	−2.3%	−6.5%	−2.5%	7.7%	0.6%
	L	−4.20%	−7.70%	−5.20%	5.70%	−0.40%		L	−2.20%	−7.20%	−4.20%	5.50%	−0.30%		L	−4.0%	−7.5%	−5.0%	6.3%	0.0%		L	−2.8%	−7.0%	−3.0%	7.4%	0.4%
Tianjin	H	−1.90%	−5.20%	−2.20%	4.90%	0.70%	Hubei	H	−1.20%	−6.20%	−2.90%	6.70%	0.10%	Jiangsu	H	−2.5%	−5.5%	−3.5%	6.7%	0.5%	Yunnan	H	−2.0%	−6.0%	−2.0%	8.0%	0.8%
	B	−2.20%	−5.70%	−2.70%	4.40%	0.50%		B	−1.70%	−6.70%	−3.40%	6.40%	−0.10%		B	−3.0%	−6.0%	−4.0%	6.4%	0.3%		B	−2.5%	−6.5%	−2.5%	7.7%	0.6%
	L	−2.50%	−6.20%	−3.20%	3.90%	0.30%		L	−2.20%	−7.20%	−3.90%	6.10%	−0.30%		L	−3.5%	−6.5%	−4.5%	6.1%	0.1%		L	−3.0%	−7.0%	−3.0%	7.4%	0.4%
Hebei	H	−0.70%	−5.20%	−2.20%	5.90%	0.50%	Hunan	H	−2.20%	−4.70%	−3.20%	6.70%	0.40%	Zhejiang	H	−4.0%	−5.5%	−3.5%	6.7%	1.0%	Shaanxi	H	−2.0%	−4.0%	−2.0%	6.0%	0.8%
	B	−1.20%	−5.70%	−2.70%	5.60%	0.30%		B	−2.70%	−5.20%	−3.70%	6.40%	0.20%		B	−4.5%	−6.0%	−4.0%	6.4%	0.8%		B	−2.5%	−4.5%	−2.5%	5.7%	0.6%
	L	−1.70%	−6.20%	−3.20%	5.30%	0.10%		L	−3.20%	−5.70%	−4.20%	6.10%	0.00%		L	−5.0%	−6.5%	−4.5%	6.1%	0.6%		L	−3.0%	−5.0%	−3.0%	5.4%	0.4%
Shanxi	H	−0.50%	−3.20%	−2.20%	5.70%	0.40%	Guangdong	H	−3.20%	−6.70%	−4.20%	5.90%	0.80%	Anhui	H	−1.5%	−5.0%	−2.5%	6.9%	1.1%	Gansu	H	−1.5%	−3.5%	−4.0%	6.2%	0.7%
	B	−1.00%	−3.70%	−2.70%	5.40%	0.20%		B	−3.70%	−7.20%	−4.70%	5.40%	0.60%		B	−2.0%	−5.5%	−3.0%	6.6%	0.9%		B	−2.0%	−4.0%	−4.5%	5.9%	0.5%
	L	−1.50%	−4.20%	−3.20%	5.10%	0.00%		L	−4.20%	−7.70%	−5.20%	4.90%	0.40%		L	−2.5%	−6.0%	−3.5%	6.3%	0.7%		L	−2.5%	−4.5%	−5.0%	5.6%	0.3%
Inner Mongolia	H	−0.50%	−4.20%	−3.70%	4.70%	0.10%	Guangxi	H	−2.70%	−4.20%	−3.20%	5.40%	0.60%	Fujian	H	−1.5%	−5.0%	−2.5%	7.0%	1.0%	Qinghai	H	−2.0%	−3.5%	−4.5%	6.2%	1.1%
	B	−1.00%	−4.70%	−4.20%	4.40%	−0.10%		B	−3.20%	−4.70%	−3.70%	5.10%	0.40%		B	−2.0%	−5.5%	−3.0%	6.7%	0.8%		B	−2.5%	−4.0%	−5.0%	5.9%	0.9%
	L	−1.50%	−5.20%	−4.70%	4.10%	−0.30%		L	−3.70%	−5.20%	−4.20%	4.80%	0.20%		L	−2.5%	−6.0%	−3.5%	6.4%	0.6%		L	−3.0%	−4.5%	−5.5%	5.6%	0.7%
Liaoning	H	−0.70%	−5.20%	−1.50%	4.40%	−0.10%	Hainan	H	−2.20%	−3.20%	−4.20%	4.70%	0.60%	Jiangxi	H	−1.3%	−4.0%	−2.0%	7.7%	0.8%	Ningxia	H	−1.0%	−3.5%	−2.5%	6.2%	1.3%
	B	−1.20%	−5.70%	−2.00%	3.90%	−0.30%		B	−1.70%	−3.70%	−4.70%	4.40%	0.40%		B	−1.8%	−4.5%	−2.5%	7.4%	0.6%		B	−1.5%	−4.0%	−3.0%	5.9%	1.1%
	L	−1.70%	−6.20%	−2.50%	3.40%	−0.50%		L	−1.20%	−4.20%	−5.20%	4.10%	0.20%		L	−2.3%	−5.0%	−3.0%	7.1%	0.4%		L	−2.0%	−4.5%	−3.5%	5.6%	0.9%
Jilin	H	−0.70%	−5.20%	−1.50%	4.40%	−0.10%	Chongqing	H	−2.70%	−7.20%	−4.20%	5.90%	0.80%	Shandong	H	−1.3%	−5.5%	−2.5%	6.3%	0.8%	Xinjiang	H	−1.8%	−3.5%	−2.5%	6.2%	1.5%
	B	−1.20%	−5.70%	−2.00%	3.90%	−0.30%		B	−2.20%	−6.70%	−4.70%	5.40%	0.60%		B	−1.8%	−6.0%	−3.0%	6.0%	0.6%		B	−2.3%	−4.0%	−3.0%	5.9%	1.3%
	L	−1.70%	−6.20%	−2.50%	3.40%	−0.50%		L	−1.70%	−6.20%	−5.20%	4.90%	0.40%		L	−2.3%	−6.5%	−3.5%	5.7%	0.4%		L	−2.8%	−4.5%	−3.5%	5.6%	1.1%
Heilongjiang	H	−0.70%	−5.20%	−1.50%	4.40%	−0.10%	Sichuan	H	−1.70%	−6.20%	−4.20%	6.70%	0.30%
	B	−1.20%	−5.70%	−2.00%	3.90%	−0.30%		B	−2.20%	−6.70%	−4.70%	6.40%	0.10%
	L	−1.70%	−6.20%	−2.50%	3.40%	−0.50%		L	−2.70%	−7.20%	−5.20%	6.10%	−0.10%

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