Carbon emission from the electric power industry in Jiangsu province,China: Historical evolution and future prediction

Abstract

This paper takes Jiangsu as an example to measure the carbon emissions from the electric power industry from 2002 to 2017, builds an extended STIRPAT model to quantify its driving factors, and uses the Monte Carlo method to simulate the evolution of carbon emissions in multiple scenarios from 2018 to 2030. The results show that: (1)Population scale, urbanization level, GDP per capita, industrial added value, and electricity consumption intensity promote the increase of carbon emissions in the electric power industry. (2)Trade openness and the transmission level of other provinces play a role in reducing carbon emissions. (3)Under the baseline scenario and the green development scenario, the carbon emissions of the electric power industry have shown a continuous growth trend, but the growth rate of carbon emissions has slowed down significantly under the green development scenario.

Keywords

electric power industry carbon emission STIRPAT model monte carlo method Jiangsu Province

1 Introduction

According to the Emissions Gap Report 2020,¹ the world is heading for a 3.2°C temperature rise this century. Since global warming has a profound impact on economic development and human health, taking urgent action to deal with it has been listed as a sustainable development goal. China, the country with the largest carbon dioxide emission at present, actively responds to climate change. At the seventy-fifth UN General Assembly, President Xi Jinping promised that China would strive to achieve the peak of carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060. According to Responding to Climate Change: China‘s Policies and Actions,² to strengthen green transformation, China incorporated the response to climate change into national economic and social development plans, curbed the haphazard development of high-emission projects, and promoted industry systems for efficient energy conservation. The role of the market was also attached with great importance. On July 16, 2021, the national carbon market started online trading, involving 2162 power generation companies and 4.5 billion tonnes of carbon dioxide emissions. China's carbon neutrality strategy of 2060 not only promotes its own high-quality development, but also provides powerful impetus for global climate governance.

During the period of 1978–2018, China's GDP grew at an average annual rate of 9.4 percent in real terms, and energy consumption and environmental pollution were behind the rapid growth in the economy, the average annual growth rate of electricity consumption in the same period was 8.65%. According to the calculation, the annual growth rate of carbon emission in China's electric power industry was 7.91% from 2000 to 2017. Therefore, the work of emission reduction in China's electric power industry is facing great pressure. Jiangsu, the country's second largest power generating province, generated 491.474 TWh electricity in 2017, with coal fired power accounting for 92.17 percent of the total, according to the National Bureau of Statistics of the People‘s Republic of China. Although it meets people's demand for electricity, this has resulted in 353.5287 million tons of related carbon emissions. Therefore, it is necessary to accurately identify the driving factors of the historical evolution of carbon emissions, simulate the potential evolution trend of carbon emissions in the future, and formulate emission reduction policies with operability.

Many scholars have carried out relevant research on the factors affecting carbon emissions in electric power industry. Table 1 shows the main characteristics of representative literature. From the perspective of the research area, it is mainly concentrated in China as a whole and provinces. From the perspective of research methods, it can be summarized as index decomposition model, structural decomposition model and econometric model. Among them, as the optimal decomposition method in the index decomposition method, the Logarithmic Mean Divisia Index (LMDI) method is widely used in the study of factors affecting carbon emission changes in the electric power industry. There are few other index decomposition methods. Yan et al.³ adopted Generalized Divisia Index Model (GDIM) to decomposing the factors affecting carbon emissions in China's electric power industry. Steenhof⁴ adopted the Laspeyres decomposition method to decomposing the influencing factors of China's electric power industry carbon emission intensity changes from 1980 to 2002 into generation mix, fossil fuel mix, energy efficiency of generation, transmission and distribution losses, and auxiliary consumption of power. Compared with the index decomposition method, there are relatively few literatures using econometric methods and structural decomposition methods to study the factors affecting carbon emissions in China's electric power industry. Zhao et al.⁵ used the co-integration model, Meng et al.⁶ constructed the logarithmic linear equation, Yan et al.⁷ and Cui et al.⁸ both constructed the STIRPAT model to study influencing factors of carbon emissions in China's electric power industry. Luo et al.⁹ and Wang et al.¹⁰ adopted SDA method to study the influencing factors of carbon emissions in China's electric power industry. From the perspective of influencing factors, it can be attributed to scale (including electricity consumption, GDP and population, etc.), structure (including thermal power structure, power generation structure and electricity production geographic distribution, etc.), and technology (including emission factor and energy intensity, etc.).

Table 1.
Summary of the main characteristics of representative literature.

Authors Time period Indicator Country/region Methods Driving factors

Karmellos et al.¹¹ 2000-2018 CO₂ EU and UK LMDI Population, GDP per capita, electricity intensity, electricity trade, generation structure, energy intensity, fuel emission factor

Luo et al.⁹ 2007-2015 CO₂ China SDA Energy efficiency, production structure, consumption structure, consumption volume

Wang et al.¹⁰ 2007-2012 CO₂ China SDA Proportion of thermal power, power generation technology, power generation structure, power demand structure, power demand

Xie et al.¹² 1985-2016 CO₂ China LMDI Emission factor, energy consumption in power generation, thermal power structure, power generation structure, transmission and distribution loss, electrification, energy intensity, economic scale

Yan et al.³ 2000-2016 CO₂ China GDIM Economic activity, energy use, electricity output, carbon intensity of GDP, carbon intensity of energy, carbon intensity of electricity, electricity intensity, energy intensity

Liao et al.¹³ 2005-2015 CO₂ 30 provinces, China LMDI Fossil fuel mix of thermal power generation, thermal efficiency of fossil fuel generation, ratio of total electricity generation from fossil fuels, electricity transmission, electricity consumption

Cui et al.⁸ 2007-2015 CO₂ China SDA Energy consumption structure, technical factor, final use structure, final use level

Goh et al.¹⁴ 1990-2014 CO₂ intensity World LMDI Electricity production, the proportion of electricity produced from fossil fuels, the share of electricity produced from fossil fuel, the generation efficiency of fossil fuel, emission factor for fossil fuel

Wang et al.¹⁵ 1995-2014 CO₂ intensity China LMDI Geographic distribution, thermal power proportion, utilization efficiency, energy composition, emission factor

Peng and Tao¹⁶ 1980-2014 CO₂ intensity China LMDI Technological innovation, structural adjustment

Cui et al.⁸ 1995-2016 CO₂ China STIRPAT Population, affluence, industrial structure, generation structure, power generation efficiency, power consuming efficiency, line loss rate

Mousavi et al.¹⁷ 2003-2014 CO₂ Iran LMDI Population, GDP per capita, economic structure, energy intensity, energy structure, the fraction of locally generated electricity, the carbon intensity of the fuel

Meng et al.⁶ 2001-2013 CO₂ China Logarithmic linear equation Total electricity consumption, non-fossil energy share of electricity generation, thermal power generation efficiency

Zhang et al.¹⁸ 2003-2013 CO₂ 30 provinces, China LMDI Emission factor, energy mix, energy intensity, generation structure, power self-sufficiency ratio, power available-to-consumed ratio, power intensity of GDP, economic activity, population

Yan et al.⁷ 1981-2013 CO₂ China STIRPAT Population, economic level, coal consumption rate, line lose rate, power generation structure, energy intensity, industrial structure, urbanization level

Wang et al.¹⁹ 1995-2012 CO₂ Shandong, China LMDI Electricity power production, power production structure, energy conversion efficiency, energy mix, emission factor

Liu et al.²⁰ 2000-2014 CO₂ intensity 30 provinces, China LMDI Fuel emission factors, fossil fuel mix, thermal efficiency of electricity generation, proportion of clean power generation, shift in regional structure of electricity generation

Zhao et al.²¹ 1985-2013 CO₂ China LMDI Emission factor, energy structure, energy intensity, capital productivity, industrial-scale

Yang and Lin²² 1985-2011 CO₂ China LMDI Carbon emission coefficient, energy efficiency for thermal power generation, electricity generation structure, electricity intensity, economic activity, population

Zhang et al.²³ 1990-2014 CO₂ China LMDI Emissions factor, energy structure, energy intensity, capital utilization efficiency, industrial scale

Yan et al.²⁴ 2000-2012 CO₂ China LMDI Energy structure, emissions intensity, energy efficiency, economic development, population

Ang et al.²⁵ 1990-2010 CO₂ intensity China LMDI Share of electricity production, real emission intensity of electricity generation

Tian and Yang²⁶ 2005-2014 CO₂ Guangdong, China LMDI Economic performance, electricity intensity factor, thermal power efficiency, coefficient of ith electricity supply, electricity supply mix

Zhou et al.²⁷ 2004-2010 CO₂ China LMDI Electricity output, regional shift, regional intensity, regional energy mix, CO2 emissions factor

Hou and Guo²⁸ 1995-2012 CO₂ Jiangsu, Zhejiang, Anhui, Shanghai, Fujian, China LMDI Electricity production, electricity generation structural, energy generation intensity

Zhang et al.²⁹ 1991-2009 CO₂ China LMDI Emissions coefficient, electricity generation efficiency, thermal power structure, electricity structure, electricity intensity, economic activity

Zhao et al.⁵ 1980-2010 CO₂ China Co-integration model The standard coal consumption rate for generating power, the standard coal consumption rate for generating power, the industrial added value of the power sector

Liu et al.³⁰ 1995-2009 CO₂ Beijing, Tianjin, Shanghai, Chongqing, China LMDI Industrial activity, activity mix, sectoral GHG intensity

Tan et al.³¹ 1998-2008 CO₂ intensity China LMDI CO₂ emission coefficient, energy intensity, power generation and consumption ratio, electricity intensity of GDP

Steenhof⁴ 1980-2002 CO₂ intensity China Laspeyres Generation mix, fossil fuel mix, energy efficiency of generation, transmission and distribution losses, auxiliary consumption of power

Malla³² 1990-2005 CO₂ China LMDI Electricity production, electricity generation structure, energy intensity of electricity generation

Authors	Time period	Indicator	Country/region	Methods	Driving factors
Karmellos et al.¹¹	2000-2018	CO₂	EU and UK	LMDI	Population, GDP per capita, electricity intensity, electricity trade, generation structure, energy intensity, fuel emission factor
Luo et al.⁹	2007-2015	CO₂	China	SDA	Energy efficiency, production structure, consumption structure, consumption volume
Wang et al.¹⁰	2007-2012	CO₂	China	SDA	Proportion of thermal power, power generation technology, power generation structure, power demand structure, power demand
Xie et al.¹²	1985-2016	CO₂	China	LMDI	Emission factor, energy consumption in power generation, thermal power structure, power generation structure, transmission and distribution loss, electrification, energy intensity, economic scale
Yan et al.³	2000-2016	CO₂	China	GDIM	Economic activity, energy use, electricity output, carbon intensity of GDP, carbon intensity of energy, carbon intensity of electricity, electricity intensity, energy intensity
Liao et al.¹³	2005-2015	CO₂	30 provinces, China	LMDI	Fossil fuel mix of thermal power generation, thermal efficiency of fossil fuel generation, ratio of total electricity generation from fossil fuels, electricity transmission, electricity consumption
Cui et al.⁸	2007-2015	CO₂	China	SDA	Energy consumption structure, technical factor, final use structure, final use level
Goh et al.¹⁴	1990-2014	CO₂ intensity	World	LMDI	Electricity production, the proportion of electricity produced from fossil fuels, the share of electricity produced from fossil fuel, the generation efficiency of fossil fuel, emission factor for fossil fuel
Wang et al.¹⁵	1995-2014	CO₂ intensity	China	LMDI	Geographic distribution, thermal power proportion, utilization efficiency, energy composition, emission factor
Peng and Tao¹⁶	1980-2014	CO₂ intensity	China	LMDI	Technological innovation, structural adjustment
Cui et al.⁸	1995-2016	CO₂	China	STIRPAT	Population, affluence, industrial structure, generation structure, power generation efficiency, power consuming efficiency, line loss rate
Mousavi et al.¹⁷	2003-2014	CO₂	Iran	LMDI	Population, GDP per capita, economic structure, energy intensity, energy structure, the fraction of locally generated electricity, the carbon intensity of the fuel
Meng et al.⁶	2001-2013	CO₂	China	Logarithmic linear equation	Total electricity consumption, non-fossil energy share of electricity generation, thermal power generation efficiency
Zhang et al.¹⁸	2003-2013	CO₂	30 provinces, China	LMDI	Emission factor, energy mix, energy intensity, generation structure, power self-sufficiency ratio, power available-to-consumed ratio, power intensity of GDP, economic activity, population
Yan et al.⁷	1981-2013	CO₂	China	STIRPAT	Population, economic level, coal consumption rate, line lose rate, power generation structure, energy intensity, industrial structure, urbanization level
Wang et al.¹⁹	1995-2012	CO₂	Shandong, China	LMDI	Electricity power production, power production structure, energy conversion efficiency, energy mix, emission factor
Liu et al.²⁰	2000-2014	CO₂ intensity	30 provinces, China	LMDI	Fuel emission factors, fossil fuel mix, thermal efficiency of electricity generation, proportion of clean power generation, shift in regional structure of electricity generation
Zhao et al.²¹	1985-2013	CO₂	China	LMDI	Emission factor, energy structure, energy intensity, capital productivity, industrial-scale
Yang and Lin²²	1985-2011	CO₂	China	LMDI	Carbon emission coefficient, energy efficiency for thermal power generation, electricity generation structure, electricity intensity, economic activity, population
Zhang et al.²³	1990-2014	CO₂	China	LMDI	Emissions factor, energy structure, energy intensity, capital utilization efficiency, industrial scale
Yan et al.²⁴	2000-2012	CO₂	China	LMDI	Energy structure, emissions intensity, energy efficiency, economic development, population
Ang et al.²⁵	1990-2010	CO₂ intensity	China	LMDI	Share of electricity production, real emission intensity of electricity generation
Tian and Yang²⁶	2005-2014	CO₂	Guangdong, China	LMDI	Economic performance, electricity intensity factor, thermal power efficiency, coefficient of ith electricity supply, electricity supply mix
Zhou et al.²⁷	2004-2010	CO₂	China	LMDI	Electricity output, regional shift, regional intensity, regional energy mix, CO2 emissions factor
Hou and Guo²⁸	1995-2012	CO₂	Jiangsu, Zhejiang, Anhui, Shanghai, Fujian, China	LMDI	Electricity production, electricity generation structural, energy generation intensity
Zhang et al.²⁹	1991-2009	CO₂	China	LMDI	Emissions coefficient, electricity generation efficiency, thermal power structure, electricity structure, electricity intensity, economic activity
Zhao et al.⁵	1980-2010	CO₂	China	Co-integration model	The standard coal consumption rate for generating power, the standard coal consumption rate for generating power, the industrial added value of the power sector
Liu et al.³⁰	1995-2009	CO₂	Beijing, Tianjin, Shanghai, Chongqing, China	LMDI	Industrial activity, activity mix, sectoral GHG intensity
Tan et al.³¹	1998-2008	CO₂ intensity	China	LMDI	CO₂ emission coefficient, energy intensity, power generation and consumption ratio, electricity intensity of GDP
Steenhof⁴	1980-2002	CO₂ intensity	China	Laspeyres	Generation mix, fossil fuel mix, energy efficiency of generation, transmission and distribution losses, auxiliary consumption of power
Malla³²	1990-2005	CO₂	China	LMDI	Electricity production, electricity generation structure, energy intensity of electricity generation

In 2017, the National Development and Reform Commission issued the Notice of the General Office of the National Development and Reform Commission on Comprehensively Promoting Trans-provincial, Trans-regional and Regional Power Transmission Price Reform, proposing to further promote the inter-provincial power market transaction. At present, Beijing-Tianjin-Hebei-Shandong, Jiangsu-Zhejiang-Shanghai and other regions are facing the obvious power supply tension, and the role of inter-provincial power transmission will become more prominent. In 2017, Jiangsu consumed 580.8 billion kW·h of electricity and generated 488.5 billion kW·h of electricity. The difference between power generation and power consumption ranked first in above regions. The contradiction between supply and demand in Jiangsu is the most prominent. Examining the impact of inter-provincial power transmission on Jiangsu's carbon emissions will help comprehensively identify the factors affecting the evolution of carbon emissions.

Measurable goals are a recommended standard, especially for those aimed at facilitating energy transition, and setting quantitative targets that must be met over time is becoming commonplace.³³ The prediction of the future evolution trend of carbon emissions helps to grasp the size and time of the peak carbon emissions. This will help us adjust our energy policy dynamically. Table 2 shows the relevant literature on carbon emission forecasts in the electric power industry. It can be seen that the research area is mainly China as a whole, and the forecast indicators are mainly carbon emissions. The forecasting methods mainly include the time series methods,^34,35 LEAP models,^36,37 and Energy system models^38–40 etc. Some scholars have set up different scenarios to predict the future evolution of carbon emissions in the electric power industry.^26,41 However, existing scenario analysis assumes that the future rate of change of carbon emission factors is a fixed single value, which is not in line with reality, and should be uncertain and a range of values. Monte Carlo method, put forward by Metropolis and Ulam⁴² is an analysis method to deal with uncertainty. It randomly selects and combines variables according to a certain probability, and then measures the target variable. If the scenario analysis is combined with the Monte Carlo method, the evolution trend and probability of carbon emissions in the electric power industry under different policy scenarios can be calculated, and based on the actual situation of the region, a scientifically feasible emission reduction path can be identified.

Table 2.

Literature on carbon emission forecasting of power industry.

Authors	Country/region	Indicator	Time period	Method	Scenarios
Demetriou and Hadjistassou³⁵	China	CO₂	2015-2050	Holt-Winters method	Business as usual (BAU) scenario, the goals scenario, the renewables (RESc) scenario, the coal free (CFr) scenario
Ikram et al.⁴³	China and America	CO₂	2017-2026	Three novel grey models	No
Yu et al.³⁸	China	CO₂	2020-2050	Energy system optimization model	The reference (REF) scenario, the structural emission reduction (SER) scenario, the technical emission reduction (TER) scenario and the co-control (COC) scenario
Tao et al.⁴⁰	China	CO₂	2020-2030	A technologies-based bottom-up CO2 mitigation model	Business as usual (BAU), control policy weak (CPW), control policy middle (CPM), control policy super(CPS)
Liu et al.³⁹	China	CO₂	2016-2035	Energy system model	One business as usual (BAU) scenario and three peak scenarios (PK)
Şahin⁴⁴	Turkey	CO₂	2017-2021	Time series method	Scenario 1-2
Wen et al.⁴⁵	China	CO₂	2020-2030	Backcasting model	No
Zhao et al.²¹	China	CO₂	2014-2030	Growth rate model based on decomposition method	Business as usual (BAU) scenario, ideal scenario, current policy scenario
Meng et al.⁶	China	CO₂	2016-2030	Small-sample hybrid model	Scenario 1-5
Tian and Yang²⁶	Guangdong, China	CO₂	2020	Growth rate model based on decomposition method	Scenario 1-4
Gu et al.⁴¹	China	CO₂	2015,2020	Growth rate model based on decomposition method	Business as usual (BAU) scenario, current policy scenario, low-carbon policy scenario
Liu et al.³⁴	China	CO₂ intensity	2020	the combination of Rolling-GM(1,1), ARIMA and SOPR	No
Cai et al.³⁶	China	CO₂	2000-2020	LEAP(Long-range Energy Alternative Planning System)	Pre-2000 policy scenario, recent policy scenario, advanced options scenario.
Cai et al.³⁷	China	CO₂	2000-2030	LEAP, ‘‘Long-range Energy Alternative Planning System’’	Baseline scenario, current policy scenario, new policy scenario

Jiangsu is the China's second largest power generating province. We hope to take Jiangsu province as a typical case, and study the influencing factors of carbon emission from the power industry, especially the power transmission. Based on this, this paper takes Jiangsu as the research object, measures carbon emissions in the electric power industry, and builds a STIRPAT model to identify the factors affecting the historical evolution of carbon emissions. Furthermore, we use Monte Carlo method to simulate the future evolution of carbon emissions under multiple scenarios, and judges scientifically feasible emission reduction paths. The main contributions of this paper are in the following two aspects: (1) Firstly investigate the impact of the inter-provincial power transmission on the historical evolution of carbon emissions in Jiangsu's electric power industry. The result provides a theoretical basis for China to speed up the construction of inter provincial power transmission. (2) Combine the scenario analysis method with the Monte Carlo method, consider the future evolution trend of carbon emissions in the electric power industry under uncertain condition, which provides a scientific basis for choosing the best emission reduction path.

2 Method, variable and data

2.1 Carbon emission measurement of electric power industry

Since thermal power generation is the main source of carbon emissions from the electric power industry in Jiangsu, this paper will not consider the carbon emissions generated by other forms of power generation. The carbon emission calculation model of the electric power industry constructed in this paper is shown in formula (1).

C = \sum_{i}^{12} E_{i} \times H_{i} \times C_{i} \times O_{i} \times \frac{44}{12}

(1)

Here, C is the carbon emission of the electric power industry, and the subscript i is the type of energy (raw coal, other washed coal, coal gangue, coke oven gas, blast furnace gas, converter gas, diesel, fuel oil, petroleum coke, refinery dry gas, other petroleum products, natural gas),

E_{i}

is the consumption of the i th energy,

H_{i}

is the average low calorific value of the i th energy,

C_{i}

is the carbon content per calorific value of the i th energy,

O_{i}

is the carbon oxidation factor of the i th energy. Due to the differences in fuel classification and carbon oxidation rate of oil gas combustion equipment, the carbon emission correlation coefficient provided by the United Nations Intergovernmental Panel on Climate Change (IPCC) is not completely consistent with China's reality. Therefore, the relevant calculations coefficient in this paper refers to the research results of Fu and Qi.⁴⁶

2.2 STIRPAT model

The IPAT model was proposed by Ehrlich and Holdren⁴⁷ to explain the relationship between population, wealth and environmental pressure, as shown in formula (2):

I = P A T

(2)

Here,

I

P

, A and T are human impact, population, affluence and technology, respectively. The STIRPAT model is an extension of the traditional IPAT model, and its formula is as follows:

I = a P^{b} A^{c} T^{d} e

(3)

Here, a is the constant term of the model,

b

, c and d are the exponential terms of

P

A

, and T respectively, and e is the error term. Take the logarithm of both sides of formula (3) to get:

\ln I = \ln a + b \ln P + c \ln A + d \ln T + \ln e

(4)

This paper combines the relevant influencing factors of carbon emissions in the electric power industry in Jiangsu to expand the STIRPAT model, considering the four aspects of population (

P

), affluence (

A

), technology (

T

) and openness (

O

). The specific ideas are as follows:

Population ( $P$ ). Consider two factors: population scale ( $P_{1}$ ) and urbanization level ( $P_{2}$ ). Cooking, clothing, housing and transportation are inseparable from energy. The increase of population scale leads to the increase of energy demand. Power production facilities increased accordingly. Greater power production leads to increased carbon emissions. The increase in the level of urbanization, on the one hand, promotes power infrastructure construction and changes energy consumption habits, which will increase energy consumption per capita;⁴⁸ on the other hand, it will promote population distribution and agglomeration, which may bring about intensive effect of electricity consumption to reduce carbon emissions.⁴⁹

Economic development level ( $A$ ). Consider two factors: GDP per capita ( $A_{1}$ ) and industrial added value ( $A_{2}$ ). GDP per capita is a more accurate measure of the real level of economic development than GDP,⁵⁰ and economic growth may increase the demand for electricity, leading to increased carbon emissions. Industry plays a decisive role in Jiangsu's economic development, but industry is characterized by high energy consumption, especially for steel, chemical industry, cement, non-ferrous metal and other industries. Jiangsu is one of the major steel and cement producing areas in China. The industrial energy consumption accounts for more than 70% of the province's total energy consumption. It is assumed that industrial development will promote the increase of electricity consumption and carbon emissions.

Technology ( $T$ ). Consider the electricity consumption intensity ( $T$ ). Electricity consumption intensity refers to the electricity consumption per unit of GDP. The lower the power consumption intensity is, the higher the power utilization efficiency is and the fewer carbon emissions are.

Openness ( $O$ ). Consider two factors: trade openness ( $O_{1}$ ) and electricity transmission level of other provinces ( $O_{2}$ ). Trade openness refers to the ratio of total foreign trade imports and exports to regional GDP. On the one hand, developing countries may use import and export trade to improve energy environmental effects through two ways: “imported product technology spillover” and “export learning”.⁵¹ “Imported product technology spillover” means absorbing the achievements of R&D investment of trading partner countries through the import of technical knowledge carriers (machinery and equipment). “Export learning” means that export enterprises have the incentive to improve their knowledge reserves, since they are faced with more fierce competition and richer technology sources in the world market. On the other hand, developing countries have loose environmental regulations, import and export trade may aggravate its environmental pollution.⁵² Therefore, the impact of trade openness on carbon emissions in Jiangsu's electric power industry needs to be further identified. The electricity transmission level of other provinces refers to the ratio of the electricity transferred from other provinces to the electricity consumption of Jiangsu. The imported electricity can reduce the pressure on the power production side, and help reduce carbon emissions. Based on the above analysis, an extended STIRPAT model is constructed, as shown in formula (5):

C = a P_{1}^{b_{1}} P_{2}^{b_{2}} A_{1}^{c_{1}} A_{2}^{c_{2}} T^{d} O_{1}^{f_{1}} O_{2}^{f_{2}} e

(5)

Take the logarithm on both sides of formula (5) to get formula (6)

\ln C = \ln a + b_{1} \ln P_{1} + b_{2} \ln P_{2} + c_{1} \ln A_{1} + c_{2} \ln A_{2} + d \ln T + f_{1} \ln O_{1} + f_{2} \ln O_{2} + \ln e

(6)

The description of the relevant variables in formula (6) is shown in Table 3.

Table 3.

Variable selection and interpretation.

Variable	Description	Unit
Carbon emission ( $C$ )	Carbon dioxide emissions from 12 energy sources in the electric power industry	Ten thousand t
Population scale ( $P_{1}$ )	Year-end resident population	Ten thousand people
Urbanization level ( $P_{2}$ )	Ratio of urban population to resident population	%
GDP per capita ( $A_{1}$ )	Ratio of GDP to resident population	RMB Yuan / person
Industrial added value ( $A_{2}$ )	Industrial added value, adjusted based on 2002	Hundred million RMB Yuan
Electricity consumption intensity ( $T$ )	Ratio of electricity consumption to regional GDP	kW·h /Yuan
Trade openness ( $O_{1}$ )	Ratio of total foreign trade imports and exports to regional GDP	%
Electricity transmission level of other provinces ( $O_{2}$ )	Ratio of electricity transferred from other provinces to electricity consumption of Jiangsu	%

2.3 Carbon emission prediction model

Based on formula (5), set the rate of change of the population scale ( $P_{1}$ ), urbanization level ( $P_{2}$ ), GDP per capita ( $A_{1}$ ), industrial added value ( $A_{2}$ ), electricity consumption intensity ( $T$ ), trade openness ( $O_{1}$ ), and electricity transmission level of other provinces ( $O_{2}$ ) as $α$ , $β$ , $γ$ , $δ$ , $ε$ , $ζ$ and $η$ , respectively. The formula (7) can be obtained.

\begin{aligned} \begin{matrix} C_{t + 1} = a P_{t + 1, 1}^{b_{1}} \times P_{t + 1, 2}^{b_{2}} \times A_{t + 1, 1}^{c_{1}} \times A_{t + 1, 2}^{c_{2}} \times T_{t + 1, 1}^{d} \times O_{t + 1, 1}^{f_{1}} \times O_{t + 1, 2}^{f_{2}} \end{matrix} \\ = a [P_{t, 1} (1 + α)]^{b_{1}} \times [P_{t, 2} (1 + β)]^{b_{2}} \times [A_{t, 1} (1 + γ)]^{c_{1}} \times [A_{t, 2} (1 + δ)]^{c_{2}} \\ \times [T_{t, 1} (1 + ε)]^{d} \times [O_{t, 1} (1 + ζ)]^{f_{1}} \times [O_{t, 2} (1 + η)]^{f_{2}} \\ = a P_{t, 1}^{b_{1}} (1 + α)^{b_{1}} \times P_{t, 2}^{b_{2}} (1 + β)^{b_{2}} \times A_{t, 1}^{c_{1}} (1 + γ)^{c_{1}} \times A_{t, 2}^{c_{2}} (1 + δ)^{c_{2}} \\ \times T_{t, 1}^{d} (1 + ε)^{d} \times O_{t, 1}^{f_{1}} (1 + ζ)^{f_{1}} \times O_{t, 2}^{f_{2}} (1 + η)^{f_{2}} \\ = (1 + α)^{b_{1}} \times (1 + β)^{b_{2}} \times (1 + γ)^{c_{1}} \times (1 + δ)^{c_{2}} \times (1 + ε)^{d} \times (1 + ζ)^{f_{1}} \times (1 + η)^{f_{2}} \\ \times a P_{t, 1}^{b_{1}} P_{t, 2}^{b_{2}} A_{t, 1}^{c_{1}} A_{t, 2}^{c_{2}} T_{t, 1}^{d} O_{t, 1}^{f_{1}} O_{t, 2}^{f_{2}} \\ = (1 + α)^{b_{1}} \times (1 + β)^{b_{2}} \times (1 + γ)^{c_{1}} \times (1 + δ)^{c_{2}} \times (1 + ε)^{d} \times (1 + ζ)^{f_{1}} \times (1 + η)^{f_{2}} \times C_{t} \end{aligned}

(7)

The rate of change of carbon emissions is shown in formula (8)

c = (1 + α)^{b_{1}} \times (1 + β)^{b_{2}} \times (1 + γ)^{c_{1}} \times (1 + δ)^{c_{2}} \times (1 + ε)^{d} \times (1 + ζ)^{f_{1}} \times (1 + η)^{f_{2}} - 1

(8)

In order to simulate the evolution trends of carbon emissions in the electric power industry, this paper constructs three scenarios based on the historical evolution trends, the effectiveness of existing policy implementation and potential emission reductions of the above factors, namely the baseline scenario, the green development scenario and the high-quality development scenario.

2.4 Data

The time span of the data involved is 2002–2017. The relevant data sources and explanations are as follows:

The energy consumption used to measure carbon emissions in the electric power industry comes from the China Energy Statistics Yearbook, and the average low calorific value, carbon content per calorific value, and carbon oxidation factor refer to⁴⁶ research results.

Resident population, urban population, industrial added value, regional GDP, and electricity consumption are sourced from the official website of the National Bureau of Statistics of China. In order to eliminate the influence of price factors, the industrial added value and regional GDP are deflated at constant prices in 2002.

Electricity transmission of other provinces comes from the Jiangsu Energy Balance Sheet in the China Energy Statistical Yearbook. The total amount of foreign trade imports and exports is derived from the Jiangsu Statistical Yearbook, in which the total foreign trade imports and exports are converted into RMB Yuan at the exchange rate of the year, and then adjusted at the constant prices in 2002.

3 Empirical results

3.1 The evolution of carbon emission

Based on formula (1), the carbon emissions of the electric power industry in Jiangsu from 2002 to 2017 are calculated, and the evolution trend is shown in Figure 1. Overall, the electric power industry's carbon emissions are on the rise, from 10735.26 ten thousand tons in 2002 to 35352.87 ten thousand tons in 2017, with an average annual growth rate of 8.27%. Among them, there was a downward trend in 2008 and 2014.

Figure 1.

Changes in carbon emissions of the electricity power industry in Jiangsu (2002-2017).

3.2 STIRPAT regression results

In order to eliminate the influence of multicollinearity between explanatory variables on the regression results, this paper uses the principal component regression method. Standardize results $\ln P_{1}$ , $\ln P_{2}$ , $\ln A_{1}$ , $\ln A_{2}$ , $\ln T_{1}$ , $\ln O_{1}$ , $\ln O_{2}$ and $\ln C$ as $Z_{1}$ , $Z_{2}$ , $Z_{3}$ , $Z_{4}$ , $Z_{5}$ , $Z_{6}$ , $Z_{7}$ and Y respectively. The results of principal component analysis are shown in Table 4.

Table 4.
Principal component extraction summary.

Initial Extraction

Principal component Eigenvalues Percentage of Variance Cumulative percentage of variance Eigenvalues Percentage of Variance Cumulative percentage of variance

1 4.929 70.415 70.415 4.929 70.415 70.415

2 1.563 22.334 92.749 1.563 22.334 92.749

3 0.471 6.734 99.483

4 0.025 0.358 99.841

5 0.008 0.118 99.960

6 0.002 0.035 99.995

7 0.000 0.005 100.000

	Initial	Extraction
1	4.929	70.415	70.415	4.929	70.415	70.415
2	1.563	22.334	92.749	1.563	22.334	92.749
3	0.471	6.734	99.483
4	0.025	0.358	99.841
5	0.008	0.118	99.960
6	0.002	0.035	99.995
7	0.000	0.005	100.000

The cumulative contribution rate of the first two eigenvalues $λ_{1} = 4.929$ , $λ_{2} = 1.563$ has reached 92.749%. Therefore, the first two principal component vectors are extracted for load analysis. The results are shown in Table 5.

Table 5.

Initial factor loading matrix.

Factor	Principal component
Factor	1	2
Z₁	0.942	0.308
Z₂	0.967	0.227
Z₃	0.976	0.194
Z₄	0.959	0.247
Z₅	−0.755	0.596
Z₆	−0.804	0.465
Z₇	0.130	−0.864

The standard orthogonalized eigenvectors corresponding to the two eigenvalues are shown in formulas (9) and (10):

ϕ_{1} = (\frac{0.942}{\sqrt{λ_{1}}}, \frac{0.967}{\sqrt{λ_{1}}}, \frac{0.976}{\sqrt{λ_{1}}}, \frac{0.959}{\sqrt{λ_{1}}}, \frac{- 0.755}{\sqrt{λ_{1}}}, \frac{- 0.804}{\sqrt{λ_{1}}}, \frac{0.130}{\sqrt{λ_{1}}})

(9)

ϕ_{2} = (\frac{0.308}{\sqrt{λ_{2}}}, \frac{0.227}{\sqrt{λ_{2}}}, \frac{0.194}{\sqrt{λ_{2}}}, \frac{0.247}{\sqrt{λ_{2}}}, \frac{0.596}{\sqrt{λ_{2}}}, \frac{0.465}{\sqrt{λ_{2}}}, \frac{- 0.864}{\sqrt{λ_{2}}})

(10)

Among them, the feature vector is shown in Table 6.

Table 6.

The feature vector.

Eigenvalues	Corresponding feature vector
λ₁	0.424 3	0.435 6	0.439 6	0.432 0	−0.340 1	−0.362 1	0.058 6
λ₂	0.246 4	0.181 6	0.155 2	0.197 6	0.476 7	0.371 9	−0.691 1

Therefore, the first principal component $F_{1}$ and the second principal component $F_{2}$ are shown in formulas (11) and (12).

F_{1} = 0.4243 Z_{1} + 0.4356 Z_{2} + 0.4396 Z_{3} + 0.4320 Z_{4} - 0.3401 Z_{5} - 0.3621 Z_{6} + 0.0586 Z_{7}

(11)

F_{2} = 0.2464 Z_{1} + 0.1816 Z_{2} + 0.1552 Z_{3} + 0.1976 Z_{4} + 0.4767 Z_{5} + 3719 Z_{6} - 0.6911 Z_{7}

(12)

After standardization, the least squares regression analysis of the dependent variable Y on the first principal component

F_{1}

and second principal component

F_{2}

is shown in Table 7.

Table 7.

γ‘s regression results on F₁ and F₂.

Variable	Coefficient	t value
Constant term	−3.553E-15
F₁	0.403***	17.379
F₂	0.325***	7.899
R²	0.966
ADJ-R²	0.960
F	182.211

Note: *, **, *** indicate significant at the level of 10%, 5%, and 1% respectively.

It can be seen from the regression results that the constant term is approximately 0, $F_{1}$ and $F_{2}$ all passed the significance test, and we can get

Y = 0.403 F_{1} + 0.325 F_{2}

(13)

Combining formulas (11) and (12), formula (14) can be obtained

Y = 0.2511 Z_{1} + 0.2345 Z_{2} + 0.2276 Z_{3} + 0.2383 Z_{4} + 0.0179 Z_{5} - 0.0251 Z_{6} - 0.2010 Z_{7}

(14)

Revert from the standardized equation to the equation with original variables, the result is shown in equation (15)

\begin{aligned} \ln C & = - 25.6325 + 3.5788 \ln P_{1} + 0.6115 \ln P_{2} + 0.1686 \ln A_{1} + 0.1639 \ln A_{2} \\ + 0.0725 \ln T - 0.0319 \ln O_{1} - 0.4086 \ln O_{2} \end{aligned}

(15)

Further, formula (16) is obtained

C = e^{- 25.6325} P_{1}^{3.5788} P_{2}^{0.6115} A_{1}^{0.1686} A_{2}^{0.1639} T^{0.0725} O_{1}^{- 0.0319} O_{2}^{- 0.4086}

(16)

The result shows that:

The coefficient of population scale is the largest, indicating that the expansion of population size and the increase in demand for electricity for production and living are the main factors that stimulate the growth of carbon emissions in the electric power industry.

The level of urbanization is another important factor that promotes the increase of emissions. The impact of urbanization in Jiangsu on the carbon emissions of the electric power industry is more reflected in stimulating the construction of electricity facilities. The intensive effect brought about by the urbanization process is not obvious, and it cannot reduce electricity consumption.

GDP per capita also plays a positive role, indicating that there is no decoupling between economic growth and carbon emissions. Although divided by the three industries, Jiangsu has realized the “three-two-one” industrial structure since 2015, but by industry, the manufacturing industry still accounts for the highest proportion of output value, and the manufacturing industry has not fully transformed to the high-end, which has a great impact on electricity consumption and carbon emissions.

The coefficient of industrial added value is positive, because industry, especially heavy industry, needs to consume a lot of electricity, which will stimulate electricity production and carbon emissions. In recent years, Jiangsu has eliminated backward production capacity and developed high-end manufacturing industries. However, the output value of high-end manufacturing does not occupy an absolute position. Therefore, industrial development still has a high dependence on electric energy.

There is a positive relationship between electricity consumption intensity and carbon emissions in the electric power industry. Electricity consumption intensity is a technical indicator that reflects the power consumption per unit of GDP. Its coefficient is positive, indicating that the more power consumption per unit GDP, the more total carbon emissions from the power industry. Jiangsu is still in the stage of economic transformation, and high-end industries with low resource consumption and low environmental pollution are not dominant. Economic development still has a high demand for electric energy.

The coefficient of electricity transmission level of other provinces is negative, indicating that electricity transmission of other provinces can alleviate the pressure on electric supply in Jiangsu, thereby reducing carbon emissions. As a receiving place for “North-South Power Transmission” and “West-East Power Transmission", Jiangsu absorbs power from Sichuan, Shanxi and other places through UHV power transmission projects, thereby easing its pressure of power generation.

The coefficient of trade openness is negative, indicating that Jiangsu has relatively strict environmental control in import and export trade. Jiangsu encourages the import of advanced technology and equipment, and relies on foreign trade to drive economic transformation and reduce energy consumption per unit of economic growth.

3.3 Scenario analysis and prediction

Based on the STIRPAT regression results, combined with formula (8), the change rate of carbon emissions in the electric power industry c is obtained, which can be expressed by formula (17).

\begin{aligned} c & = {(1 + α)}^{3.5788} \times {(1 + β)}^{0.6115} \times {(1 + γ)}^{0.1686} \times {(1 + δ)}^{0.1639} \\ \times {(1 + ε)}^{0.0725} \times {(1 + ζ)}^{- 0.0319} \times {(1 + η)}^{- 0.4086} - 1 \end{aligned}

(17)

Based on the historical change trends of various factors and the effectiveness of current policy implementation, three scenarios are set to simulate the future evolution of carbon emissions in the electric power industry in Jiangsu, namely the baseline scenario, the green development scenario and the high-quality development scenario.

Monte Carlo simulation is widely used in the analysis of uncertain events. This method uses random numbers to solve calculation problems. It can randomly select values for reference variables and the target variable is calculated after combined. When the value interval of the variable and the most likely value result are determined, but the shape of the probability distribution is not timed, the triangular distribution is suitable for random selection of variables.⁵³ The most likely potential change rate of each factor is its intermediate value, and the relationship between the maximum, intermediate and minimum values is constructed based on the triangular distribution. This paper uses Matlab software to simulate the trend of carbon emission.

3.3.1 The baseline scenario

In the baseline scenario, it is assumed that the future annual average rate of change of each driving factor continues the past change characteristics. Referring to the research results of Shao et al.,⁵⁴ the average annual potential change rate of each factor from 2018 to 2030 is determined by referring to its average annual change rate in 2002–2017, 2005–2017, 2010–2017, and 2015–2017. The maximum and minimum values of the potential rate of change of each factor select the maximum and minimum of the above four periods. For the two average annual rates of change in the middle, the closer the cycle, the more significant the impact on the future, so select the average annual rate of change in the recent period has become the median value of the potential rate of change. Table 8 shows the setting of the potential annual average change rate of each factor in the baseline scenario.

Table 8.
The potential annual average change rate of each factor in the baseline scenario (%).

2018-2030

Minimum value Median value Maximum value

P₁ 0.29 0.33 0.54

P₂ 1.67 1.83 2.94

A₁ 7.14 8.67 10.93

A₂ 8.71 8.87 12.38

T₁ −2.74 −0.87 −0.64

O₁ −6.22 −2.07 −1.10

O₂ 1.60 3.55 5.78

	2018-2030
P₁	0.29	0.33	0.54
P₂	1.67	1.83	2.94
A₁	7.14	8.67	10.93
A₂	8.71	8.87	12.38
T₁	−2.74	−0.87	−0.64
O₁	−6.22	−2.07	−1.10
O₂	1.60	3.55	5.78

Figure 2 shows the evolution trend of carbon emissions distribution under the baseline scenario. It can be seen from the figure that the carbon emissions of the electric power industry are showing an increasing trend. In 2018, the carbon emissions range is 362.40–376.68 million tons, and the most probable carbon emissions are 368.89 million tons. By 2030, carbon emissions range is from 487.87–806.29 million tons, and the most probable carbon emissions are 610.46 million tons. This means that population, economy, technology, trade openness and so on continue to change the trends of the past, and the carbon emissions of the electric power industry in Jiangsu will continue to grow. Therefore, social and economic policies must be adjusted to reduce carbon emissions.

Figure 2.

The evolution trend of carbon emissions distribution under the baseline scenario.

3.3.2 The green development scenario

The green development scenario assumes that various indicators will change with reference to recent development trends or planning documents, that is, population scale and urbanization level are reasonably advanced, the economy is operating in accordance with the new normal, energy utilization rate has risen, and the electricity utilization rate is appropriately increased, etc. to strengthen environmental protection.

(1) Population scale

“The Thirteenth Five-Year Plan for Population Development of Jiangsu Province“ proposes that the resident population will reach 82 million in 2020. Since the resident population at the end of 2017 was 80.293 million, the average annual change rate of population from 2018 to 2020 is calculated to be 0.70%. “The Jiangsu Province Urban System Plan (2015–2030)“ proposes that the resident population in 2020 and 2030 will be 85 million and 90 million, respectively. From this calculation, the average annual change rate of population from 2021 to 2030 is 0.57%. The above average annual change rate is taken as the median value of the potential change rate from 2018 to 2020 and from 2021 to 2030, and the maximum and minimum values are obtained by increasing and decreasing by 0.1 percentage points on the basis of the median value, respectively.

(2) Urbanization level

“The Jiangsu Province Urban System Plan (2015–2030)“ proposes that the urbanization rate in 2020 and 2030 will be 72% and 80%, respectively. Since the urbanization rate in 2017 is 68.8%, we can calculate the annual average change rate of urbanization is 1.53% and 1.06% in 2018–2020 and 2021–2030 respectively. The above average annual change rate is taken as the median value of the potential change rate from 2018 to 2020 and from 2021 to 2030, and the maximum and minimum values are obtained by increasing and decreasing by 0.5 percentage points on the basis of the median value, respectively.

(3) GDP per capita

“The Outline of the 13th Five-Year Plan for National Economic and Social Development of Jiangsu Province“ sets the average annual growth rate of regional GDP during the “13th Five-Year Plan” period to be about 7.5%. From the previous paper, we can get the average annual change rate of population from 2018 to 2020 is 0.70%, so the average annual change rate in GDP per capita from 2018 to 2020 is 6.75%. From 2021 to 2030, based on Sheng and Zheng⁵⁵'s research on the feasible growth path of China's GDP and Jiangsu's important economic position, the economic growth rate of 5.76% in the high plan is selected as the average annual GDP growth rate of Jiangsu during this period. From the previous paper, the average annual change rate of population from 2021 to 2030 is 0.57%, so the average annual change rate of Jiangsu's GDP per capita from 2021 to 2030 is 5.16%. The above average annual change rate is taken as the median value of the potential GDP per capita change rate from 2018 to 2020 and from 2021 to 2030. With reference to the variation of GDP per capita growth rate in the study of Lin and Liu,⁵⁶ the maximum and minimum values are obtained by increasing and decreasing by 1 percentage point on the basis of the median value, respectively.

(4) Industrial added value

In recent years, due to the increase in labor costs, the impact of environmental remediation and inefficient industrial investment, the growth of Jiangsu's industrial economy has slowed down. Jiangsu urgently needs to realize the transformation of growth power, namely to promote industrial upgrading with talent, information, technology and other factors. Coupled with the impact of “re-industrialization” in developed countries, Jiangsu's industrial economy has generally faced greater downward pressure. 7% is regarded as the average annual change rate of industrial added value in Jiangsu from 2018 to 2020. With reference to Zhang and Shi⁵⁷'s predictions on the growth range of China's industrial added value above designated size in 2020 (5.4–5.8%), assuming that the average annual change rate of Jiangsu's industrial added value from 2021 to 2030 remains at the level of 5.8%, this is taken as the median value of the potential change rate. The maximum value and the minimum value are obtained by increasing and decreasing 1 percentage point on the basis of the median value, respectively.

(5) Electricity consumption intensity

“The Thirteenth Five-Year Plan for Electric Power Development in Jiangsu Province“ predicts that the electricity consumption of the whole society will increase by 4.9% annually during the Thirteenth Five-Year Plan, while the average annual economic growth rate during the Thirteenth Five-Year Plan period is set at 7.5%. The average annual change rate of electricity consumption intensity from 2018 to 2020 is −2.42%. It is assumed that this indicator will maintain the past trend of change from 2021 to 2030. Therefore, this change rate is taken as the median value of the potential change rate of electricity consumption intensity from 2018 to 2030. With reference to the change range of energy intensity growth in the study of Lin and Liu,⁵⁶ the maximum and minimum values are obtained by increasing and decreasing by 0.5 percentage point on the basis of the median value, respectively.

(6) Trade openness

The average economic growth rate of Jiangsu during the 13th Five-Year Plan period is set at about 7.5%. In 2017, the total foreign trade import and export volume of Jiangsu has an average annual change rate of 4.09%, compared to the 2015 data. Assuming that the effect of exchange rate changes is not taken into account, the economy will grow at the planned rate from 2018 to 2020, and the total foreign trade import and export volume will continue the growth trend of 2015–2017. And the average annual change rate of the “trade openness” indicator for 2018–2020 is calculated −3.17%, taking it as the median value of the potential change rate from 2018 to 2020. It is assumed that the trade openness from 2021 to 2030 maintains the development trend of 2018 to 2020. The maximum value and the minimum value are obtained by increasing and decreasing 1 percentage point on the basis of the median value, respectively.

(7) Electricity transmission level of other provinces

“The Thirteenth Five-Year Plan for Electric Power Development in Jiangsu Province“ predicts that the electricity consumption of the whole society will increase by 4.9% annually during the 13th Five-Year Plan. In 2017, Jiangsu transferred electricity from other provinces, compared with the 2015 data, the average annual change rate was 12.72%. Assuming that the electricity consumption in Jiangsu will grow at the expected rate from 2018 to 2020, the electricity transferred from other provinces will continue the growth trend of 2015–2017, and the average annual change rate of the transmission level of other provinces from 2018 to 2020 is calculated to be 7.45%, which is regarded as the median value of the potential change rate. Assuming that 2021–2030, the electricity diversion situation remains the same as the previous period, and the median value of the potential change rate of the transmission level of other provinces is still 7.45%. The maximum value and the minimum value are obtained by increasing and decreasing 2.5 percentage point on the basis of the median value, respectively.

Table 9 shows the setting of the potential annual average change rate of each factor in the context of green development.

Table 9.
The potential annual average change rate of each factor in the green development scenario (%).

2018-2020 2021-2025 2026-2030

Minimum value Median value Maximum value Minimum value Median value Maximum value Minimum value Median value Maximum value

P₁ 0.60 0.70 0.80 0.47 0.57 0.67 0.47 0.57 0.67

P₂ 1.03 1.53 2.03 0.56 1.06 1.56 0.56 1.06 1.56

A₁ 5.75 6.75 7.75 4.16 5.16 6.16 4.16 5.16 6.16

A₂ 6.00 7.00 8.00 4.80 5.80 6.80 4.80 5.80 6.80

T₁ −2.92 −2.42 −1.92 −2.92 −2.42 −1.92 −2.92 −2.42 −1.92

O₁ −4.17 −3.17 −2.17 −4.17 −3.17 −2.17 −4.17 −3.17 −2.17

O₂ 4.95 7.45 9.95 4.95 7.45 9.95 4.95 7.45 9.95

	2018-2020	2021-2025	2026-2030
P₁	0.60	0.70	0.80	0.47	0.57	0.67	0.47	0.57	0.67
P₂	1.03	1.53	2.03	0.56	1.06	1.56	0.56	1.06	1.56
A₁	5.75	6.75	7.75	4.16	5.16	6.16	4.16	5.16	6.16
A₂	6.00	7.00	8.00	4.80	5.80	6.80	4.80	5.80	6.80
T₁	−2.92	−2.42	−1.92	−2.92	−2.42	−1.92	−2.92	−2.42	−1.92
O₁	−4.17	−3.17	−2.17	−4.17	−3.17	−2.17	−4.17	−3.17	−2.17
O₂	4.95	7.45	9.95	4.95	7.45	9.95	4.95	7.45	9.95

Figure 3 shows the evolution trend under the green development scenario. Under the green development scenario, the carbon emissions of the electric power industry in Jiangsu are still showing a continuous growth trend. The carbon emissions in 2018 ranged from 356.99–369.43 million tons, and the most probable carbon emissions were 363.02 million tons. The range of carbon emissions in 2030 is 377.04–523.53 million tons, and the most probable carbon emissions are 440.76 million tons. From 2018 to 2030, the most likely annual change rate of carbon emissions is 1.63%. Compared with the baseline scenario, the growth rate of carbon emissions has slowed down significantly. It can be seen that, guided by the concept of green development, the government actively adopts macro-control policies to respond to environmental changes, which can slow down the growth trend of carbon emissions in Jiangsu's electric power industry, but the turning point of emissions from growth to reduction has not yet appeared.

Figure 3.

The evolution trend of carbon emission distribution of Jiangsu's electric power industry from 2018 to 2030 under the green development scenario.

3.3.3 The high-quality development scenario

The high-quality development scenario assumes that governments will increase support for foreign trade, promote high-quality economic development through “bringing in” and “going out"; increase the construction of electricity facilities between provinces, improve system design and power market reforms, further promote the cross-regional deployment of energy. In terms of technology, we will promote the transformation of industrial structure and improve energy efficiency. In this scenario, the average annual change rate of each factor is as follows (assuming the fluctuation of the potential change rate of each factor is the same as the green development scenario).

(1) Population scale

Under the premise that the economy has reached a relatively high level, the change in population scale is relatively stable. This paper assumes that the population scale change trend under the high-quality development scenario is the same as the green development scenario.

(2) Urbanization level

The green development scenario has assumed that Jiangsu is focusing on the development of new urbanization and emphasizing “energy saving, intensivism and ecological protection “. It is assumed that the urbanization level change trend under the high-quality development scenario is the same as the green development scenario.

(3) GDP per capita

China has launched “made in China 2025” as the action program of “manufacturing power strategy". Promoting the transformation of manufacturing to high-end is conducive to maintaining the medium-to-high speed of economic growth. It is assumed that the development trend of GDP per capita from 2018 to 2025 is the same as the green development scenario. After 2025, with the upgrading of the industrial structure brought about by technological breakthroughs, the economy will usher in a new round of growth. It's assumed that the average annual change rate of GDP per capita from 2026–2030 increases by 1 percentage point compared to the green development scenario and 6.16% is used as the median value of the potential change rate.

(4) Industrial added value

Jiangsu promulgated the “Made in China 2025 Jiangsu Action Plan”, which required the promotion of intelligent manufacturing and the capture of key technologies in various fields to improve the manufacturing level. It is assumed that the potential annual change rate of Jiangsu's industrial added value from 2018 to 2025 is the same as that of the green development scenario. After 2025, Jiangsu will make breakthroughs in the field of advanced manufacturing, the manufacturing industry will transition to high-end, and the industrial economy will usher in a new round of growth. Assuming the potential change rate of industrial added value increased by 1% compared with the green development scenario in the same period.

(5) Electricity consumption intensity

Through technological research and development and industrial restructuring, the energy consumption per unit of economic growth is reduced. However, it will take some time for the technology to develop, break through, and mature. Assuming that the change trend of electricity consumption intensity from 2018 to 2020 is the same as that of the green development scenario, the technical effects will gradually become prominent from 2021 to 2025. The potential change rate of electricity consumption intensity is reduced by 0.5 percentage points from the green development scenario during the same period, and −2.92% is recorded as the median potential change rate. Assuming further breakthroughs in the technical field after 2025, 0.5 percentage points are subtracted from −2.92%, and −3.42% is used as the median value of the potential change rate of electricity consumption intensity.

(6) Trade openness

It is assumed that the changing trend and scenario of trade openness in 2018–2020 will be the same. From 2021 to 2025, assuming that foreign trade is supported by policies, the trade openness will increase by 1 percentage point compared to the scenario, with −2.17% as the median value of the potential change rate. From 2026 to 2030, due to the upgrading of manufacturing industry to enhance China's international competitiveness in trade, change China's position in the international division of labor, and stimulate the development of foreign trade economy, assuming a further increase in trade openness by 1 percentage point, and −1.17% is used as the median value of the potential change rate.

(7) Electricity transmission level of other provinces

According to the national grid planning and deployment, the AC synchronous power grids in North China, Central China, East China, Northeast China, and Northwest China will be integrated into the eastern and western power grids in 2020; in 2025, the eastern and western power grids will be combined into one synchronous power grid. The construction of this national energy Internet will help Jiangsu to further absorb electricity from other regions. Assuming that the change trend of the electricity transmission level of other provinces from 2018 to 2020 is the same as that of the green development scenario, the electricity transmission level of other provinces in 2021–2025 will increase by 2.5 percentage points on the basis of the green development scenario, taking 9.95% as the median value of the potential change rate; In 2026–2030, after the establishment of the national synchronous grid, it is assumed that the electricity transmission level of other provinces will be further increased by 2.5%, with 12.45% as the median value of the potential change rate.

Table 10 shows the setting of the potential annual average change rate of each factor under the high-quality development scenario.

Table 10.
The potential annual average change rate of each factor under the high-quality development scenario (%).

2018-2020 2021-2025 2026-2030

Minimum value Median value Maximum value Minimum value Median value Maximum value Minimum value Median value Maximum value

P₁ 0.60 0.70 0.80 0.47 0.57 0.67 0.47 0.57 0.67

P₂ 1.03 1.53 2.03 0.56 1.06 1.56 0.56 1.06 1.56

A₁ 5.75 6.75 7.75 4.16 5.16 6.16 5.16 6.16 7.16

A₂ 6.00 7.00 8.00 4.80 5.80 6.80 5.80 6.80 7.80

T₁ −2.92 −2.42 −1.92 −3.42 −2.92 −2.42 −3.92 −3.42 −2.92

O₁ −4.17 −3.17 −2.17 −3.17 −2.17 −1.17 −2.17 −1.17 −0.17

O₂ 4.95 7.45 9.95 7.45 9.95 12.45 9.95 12.45 14.95

	2018-2020	2021-2025	2026-2030
P₁	0.60	0.70	0.80	0.47	0.57	0.67	0.47	0.57	0.67
P₂	1.03	1.53	2.03	0.56	1.06	1.56	0.56	1.06	1.56
A₁	5.75	6.75	7.75	4.16	5.16	6.16	5.16	6.16	7.16
A₂	6.00	7.00	8.00	4.80	5.80	6.80	5.80	6.80	7.80
T₁	−2.92	−2.42	−1.92	−3.42	−2.92	−2.42	−3.92	−3.42	−2.92
O₁	−4.17	−3.17	−2.17	−3.17	−2.17	−1.17	−2.17	−1.17	−0.17
O₂	4.95	7.45	9.95	7.45	9.95	12.45	9.95	12.45	14.95

Figure 4 shows the evolution trend under the high-quality development scenario. Under this scenario, the carbon emissions of the electric power industry in Jiangsu show a trend of increasing first and then decreasing. The range of carbon emissions in 2018 is 357.50–368.46 million tons, and the most probable carbon emissions are 362.92 million tons. The range of carbon emissions in 2025 is 350.85–434.95 million tons, and the most probable carbon emissions are 390.80 million tons and reach a peak. The range of carbon emissions in 2030 is 333.43–449.35 million tons, and the most probable carbon emissions are 386.17 million tons. The annual average change rates of carbon emissions with the highest probability in 2018–2025 and 2025–2030 are 1.06% and −0.24%, respectively. The inflection point of this scenario appears after 2025.

Figure 4.

The evolution trend of carbon emissions distribution under the high-quality development scenarios.

4 Conclusions and policy implications

Based on the extended STIRPAT model, this paper analyzes the driving factors of carbon emissions from the electric power industry in Jiangsu from 2002 to 2017, and uses the Monte Carlo method to simulate the evolution trend from 2018 to 2030. The results show that: Firstly, population scale, urbanization level, GDP per capita, industrial added value, and electricity consumption intensity promote the increase of carbon emissions in the electric power industry in Jiangsu. Among them, the effect of the population scale and urbanization level is relatively significant. Secondly, trade openness and the transmission level of other provinces play a role in reducing carbon emissions. Among them, the effect of the transmission level of other provinces is relatively significant. Thirdly, under the baseline scenario and the green development scenario, the carbon emissions of the electric power industry in Jiangsu have shown a continuous growth trend, but the growth rate of carbon emissions has slowed down significantly under the green development scenario. Fourthly, the carbon emissions will reach an inflection point in 2025 under the high-quality development scenario, and the expansion of the electricity transmission and the improvement of energy efficiency have gradually become more prominent in reducing carbon emissions.

Based on different scenarios, the policy recommendations proposed are as follows:

According to the baseline scenario, if the past development model is followed, the carbon emissions of the electric power industry in Jiangsu will continue to grow, so the policy should be adjusted in time. Combined with the analysis of driving factors, it can be seen that the formulation of emission reduction policies can focus on population, urbanization, industrial structure, and foreign trade. From the perspective of population and urbanization, Jiangsu should adhere to people-oriented, green and low-carbon concepts in urban and rural construction, form a moderately population agglomeration pattern, and reasonably plan electricity supporting facilities to minimize electricity waste.

According to the green development scenario, adopting measures such as reasonable population planning, improving energy utilization, and strengthening electricity transmission can significantly slow down the growth rate of carbon emissions in the electric power industry. From the perspective of technology and industrial structure, (1) The government should accelerate the adjustment of industrial structure, develop the tertiary industry and high-end manufacturing, abandon the extensive economy, and reduce the electricity required for unit economic growth; (2) Insist on the strategy of innovation-driven development, strengthen cooperation with universities and scientific research institutions, and encourage research in areas such as energy saving and emission reduction, intelligent production.

According to the high-quality development scenario, under the premise of the government's promotion of the transformation of the industrial structure, the vigorously increasing of the electricity transmission, and the expansion of foreign trade, the electric power industry in Jiangsu has a large room for emission reduction. From the perspective of foreign trade and electricity transmission of other provinces, (1) Jiangsu should vigorously develop foreign trade, and realize the upgrading of foreign trade with the strategy of “quality imports and quality exports”. On the one hand, it is necessary to strengthen ties with traditional foreign trade markets such as the United States, the European Union, and Japan, actively introduce high-tech and concepts, and strengthen international cooperation in energy technology and environmental management. On the other hand, it is necessary to guide the manufacturing industry to transform from a production-oriented one to a production-service-oriented one, make production intelligent and green, and encourage the export of high-quality and high-value-added products, reduce the production of high-energy-consuming products; (2)Ensure cross-provincial electricity transmission through UHV AC and DC projects and ease the pressure on electricity supply in Jiangsu; accelerate grid integration, and establish a national energy Internet; reform the electricity price formation mechanism and promote power trading marketization to optimize resource allocation.

Carbon emission is a global environmental problem, which is related to global sustainable development. The study of carbon emissions in other regions, such as the Asia Pacific, is indeed of great significance to global sustainable development. We will consider the carbon emissions of the world in the follow-up research.

Footnotes

Acknowledgement

This research is supported by Introduction of Doctoral Research Start-up Fees (2021) (No.: 1042932103).

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 Introduction of Doctoral Research Start-up Fees (2021) (No.: 1042932103).,

ORCID iD

Yung-Ho Chiu

Chenjun Zhang is a lecturer in Jiangsu University of Science and Technology. His research interests are in the areas of water resource economics, carbon neutrality and environment economics. He received his PhD in Management Science and Engineering from Hohai University.

Teli Ma, undergraduate at School of Business in Hohai University. His research focuses on energy management and environmental economics.

Changfeng Shi is a lecturer at School of Business in Hohai University. His major is Management Science and Engineering. The research interests of him are water resource management, carbon neutrality and energy economics.

Yung-Ho Chiu is a professor of Department of Economics at Soochow University, Taiwan. In 1994, he received his PhD degree in Economics from University of Mississippi, USA. His current research interests include financial management, banking analysis, risk management, China economy research, operations research and green economics. He has published more than 150 academic articles or papers in various periodical.

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	Initial			Extraction
Principal component	Eigenvalues	Percentage of Variance	Cumulative percentage of variance	Eigenvalues	Percentage of Variance	Cumulative percentage of variance
1	4.929	70.415	70.415	4.929	70.415	70.415
2	1.563	22.334	92.749	1.563	22.334	92.749
3	0.471	6.734	99.483
4	0.025	0.358	99.841
5	0.008	0.118	99.960
6	0.002	0.035	99.995
7	0.000	0.005	100.000

	2018-2030
	Minimum value	Median value	Maximum value
P₁	0.29	0.33	0.54
P₂	1.67	1.83	2.94
A₁	7.14	8.67	10.93
A₂	8.71	8.87	12.38
T₁	−2.74	−0.87	−0.64
O₁	−6.22	−2.07	−1.10
O₂	1.60	3.55	5.78

Carbon emission from the electric power industry in Jiangsu province,China: Historical evolution and future prediction

Abstract

Keywords

1 Introduction

2.1 Carbon emission measurement of electric power industry

3 Empirical results

3.1 The evolution of carbon emission

Table 8. The potential annual average change rate of each factor in the baseline scenario (%). 2018-2030 Minimum value Median value Maximum value P1 0.29 0.33 0.54 P2 1.67 1.83 2.94 A1 7.14 8.67 10.93 A2 8.71 8.87 12.38 T1 −2.74 −0.87 −0.64 O1 −6.22 −2.07 −1.10 O2 1.60 3.55 5.78

Footnotes

Acknowledgement

Declaration of conflicting interests

Funding

ORCID iD

References