Analysis of Interprovincial Differences in CO₂ Emissions and Peak Prediction in the Yangtze River Delta

Abstract

The Yangtze River Delta is the most populous and economically active region in China. Studying the reduction in CO₂ emissions in this region is of great significance in achieving the goal of “peak carbon and carbon neutrality” in China. In this study, the Tapio decoupling and extended STIRPAT models were used to study the differences in provincial CO₂ emissions characteristics and influencing factors in the Yangtze River Delta from 2001 to 2019. The results show that the growth rate of CO₂ emissions was slower than that of economic development, which means that CO₂ emissions and economic growth were in a state of weak decoupling. As found by ridge regression, the same factor has different impacts on CO₂ emissions among provinces. The differences in these influencing factors were mainly caused by the imbalance of development in the Yangtze River Delta. Nine development scenarios were set out to predict the future trend of CO₂ emissions based on economic development and carbon emissions technology using the extended STIRPAT model. It was found that low-carbon-emissions technology is conducive to controlling CO₂ emissions in the Yangtze River Delta. In that case, the CO₂ emissions would peak in 2029 at 1895.78~1908.25 Mt. Compared with the low-carbon-emissions scenarios, both the medium- and high-carbon-emissions scenarios are not conducive to achieving a carbon peak, with a 2~5-year delay in peak time and increasing emissions by 3.69~7.68%. In order to reduce the Yangtze River Delta’s CO₂ emissions and pass the peak emissions as soon as possible, it is essential to not only optimize the energy structure, upgrade industries and promote the coordinated development of low-carbon technologies, but also promote emissions reduction in the transportation and construction fields and advocate for a low-carbon lifestyle among the public.

1. Introduction

With the continuous growth of the global economy, the carrying capacity of resources and the environment is constantly being challenged. Under increasingly serious problems such as climate warming and environmental degradation, CO₂ emissions reduction has become a major global issue of concern to all countries around the world [1,2]. As the largest developing country, China’s CO₂ emissions have increased yearly [3,4]. At present, the proportion of human-induced CO2 emissions in China is approximately 30% of global emissions. Consequently, China is playing an essential role in global emissions reduction and climate change mitigation, and the Chinese government has also promised that its CO₂ emissions will peak by 2030 [5,6].

The Yangtze River Delta refers to Shanghai City (a municipality directly under the control of the Central Government), Zhejiang Province, Jiangsu Province and Anhui Province, comprising the lower plain of the Yangtze River, with an area of 350.8 thousand square kilometers, less than 4% of China’s land area. In 2020, the population of the Yangtze River Delta was 235 million, 16.6% of China’s total population. Moreover, the total regional economic gross domestic product (GDP) has reached CNY 24.47 trillion, 24% of China’s total economic GDP, making it one of the most populous and economically active regions in China. In the face of China’s approaching carbon peak and the realistic demand for promoting sustainable economic development, how the Yangtze River Delta coordinates the contradiction between economic development and resource and environmental constraints effectively ensures high-quality economic development and takes the lead in achieving the goal of carbon peaking which, to a certain extent, will affect the process of China’s overall realization of the “dual carbon” goal [7,8]. Therefore, a quantitative study of CO₂ emissions and their influencing factors in the Yangtze River Delta will help formulate reasonable emissions reduction policies, guide the regional economy in developing more rationally and help the region to achieve the target of green and low-carbon development as early as possible.

Against the background of carbon peak and carbon neutrality, the influencing factors and trends of CO₂ emissions have been hot topics in research. The Kaya identity and the LDMI method are used to analyze the factors affecting CO₂ emissions [9,10]. Logistics models, grey models and CGE models are often used for CO₂ emissions prediction [11,12]. The IPAT and STIRPAT models can be applied for both factor analysis and prediction and are common methods [13,14]. In the 1970s, American ecologists Ehrlich and Holden first proposed the IPAT model to assess environmental pressure. This model shows that population, economy and technology, as well as the interaction among them, will significantly impact the environment [15,16]. However, it is hard to explain the non-proportional relationship of the impact on the environment caused by different influencing factors [17,18].

Based on the IPAT model, York and Dietz et al. proposed the STIRPAT model, which considers the effect of different changes in population, economy and technology on the environment, eliminating the impact of the non-proportional change [14,19]. The model has been widely recognized and widely used by the academic community. The extended STIRPAT model introduces multiple influencing factors to study CO₂ emissions, such as population size, GDP per capita, level of urbanization, industry and energy structure, government policies and so on [13,20]. Diaz et al. analyzed the trend changes of Spain’s energy intensity ratio based on the extended STIRPAT model of population, affluence and technological factors and identified that energy intensity change is the main driver of greenhouse gas emissions [21]. Hassan et al. used panel data to study the impact of urbanization, energy and power consumption and GDP per capita on CO₂ emissions in all BRICS countries from 1990 to 2014 [22]. Bekhet and He conducted empirical research on Malaysia and China, respectively, and found that there is an inverted U-shaped relationship existing between urbanization and CO₂ emissions [23,24]. Bargaoui et al. found that the impact of economic development, growth of population, level of urbanization and the Kyoto Protocol on CO₂ emissions varied with national income levels by using panel data from 214 countries from 1980 to 2010 [25]. Zeng et al. confirmed that the population, GDP, energy consumption and industry structure had significant effects on energy CO₂ emissions in the Chengdu–Chongqing urban agglomeration [12].

To sum up, the influencing factors of CO₂ emissions vary significantly from region to region. The Yangtze River Delta is an economically advanced region [26,27], and many scholars have studied CO₂ emissions and their influencing factors in this region [28,29]. Xiao et al. argued that economic development and energy intensity had significant effects on CO₂ emissions. To some extent, the inflow of foreign direct investment reduced the per capita CO₂ emissions in the Yangtze River Delta [30]. Yu et al. found that urban size had a negative correlation with CO₂ emissions, technology progress and opening up, and the population had spatial dependence and spatial spillover effects on CO₂ emissions [31]. Xue et al. concluded that energy intensity was the most influential factor, followed by GDP per capita, with a higher contribution to CO₂ emissions than the population and urbanization rate in the Yangtze River Delta during the period 1990–2011 [32]. However, few previous studies have explored the interprovincial differences in CO₂ emissions in the Yangtze River Delta and the impact of such differences on the regional carbon peak. The growth of population, the development of the economy, science and technology, the industry structure and the urbanization ratio are unbalanced between the provinces and city in the Yangtze River Delta [26,33], which may lead to different effects on the provincial CO₂ emissions and the peak. Therefore, this paper analyzes the status and trend of CO₂ emissions in the Yangtze River Delta by using the provincial panel data from 2001 to 2019 and studies the influencing factors and their degree of influence on provincial CO₂ emissions. The peak amount and peak time of CO₂ in the provinces and city are predicted under different scenarios based on the provincial economic growth and the development of carbon emissions technology. On the basis of considering the peak time and cumulative emissions, relevant measures and suggestions are put forward.

2. Data Sources and Methods

2.1. Data Sources

The provincial panel data of residential population, GDP, GDP per capita, energy consumption, GDP index and GDP per capita index were from the Shanghai Statistical Yearbook, Zhejiang Province Statistical Yearbook, Jiangsu Province Statistical Yearbook and Anhui Province Statistical Yearbook. Selecting the data from 2001 to 2019 as research data, GDP and GDP per capita were calculated at comparable prices in 2010 using their respective indexes, and the energy intensity was also calculated at comparable prices.

2.2. Calculation Method of CO₂ Emissions

CO₂ emissions data were obtained from the China Carbon Accounting Database (CEADs). The CO₂ emissions in this database were estimated by the IPCC administrative territory method. CO₂ emissions inventories include energy- and process-related (cement) CO₂ emissions [6,34]. The energy-related emissions were counted on the basis of the fossil fuel combustion in sectors of the national economy [35]. The CO₂ emissions calculation formula is shown in Equation (1).

$${\mathrm{CO}}_2={\displaystyle \sum \mathrm{CE}\mathrm{ij}={\displaystyle \sum \mathrm{AD}\mathrm{ij}\times \mathrm{NCV}\mathrm{i}\times \mathrm{CC}\mathrm{i}\times \mathrm{O}\mathrm{ij}}}$$

(1)

CO₂: the sum of CO₂ emissions generated by energy resource combustion;

CEij: the CO₂ emissions from fossil fuel i burnt in sector j; i refers to 17 kinds of fossil fuel, and j refers to the 47 sectors of the national economy in China;

ADij: fossil fuel consumption by the corresponding fossil fuel types and sectors;

NCVi: the heat value produced per physical unit of fossil fuel combustion;

CCi: the CO₂ emissions per net caloric value produced by fossil fuel i;

Oij: the oxidation ratio during fossil fuel combustion.

2.3. STIRPAT Model

The standard form of the STIRPAT model is:

I = aP^bA^cT^de

(2)

where I is environmental impact; P, A and T refer to population, wealth and technology, respectively; a is the coefficient of the model; b, c and d represent the index; and e indicates the error term.

After taking the logarithm of both sides of Equation (2), Equation (3) can be obtained:

lnI = lna + blnP + clnA + dlnT + lne

(3)

To investigate the effects on CO₂ emissions, Equation (4) was constructed by expanding Equation (3) based on the data analysis.

lnI = lna + blnP + clnA + dlnT + elnS + lnf

(4)

where I is the CO₂ emissions (Mt) and P represents the residential population (10⁴); A is the wealth expressed in GDP per capita (CNY); T is the energy intensity, meaning the energy consumption per unit of GDP (tonnes of standard coal equivalent/CNY 10⁴); and S is the energy structure, expressed in terms of the ratio of energy-related CO₂ emissions to energy consumption (tonnes CO₂/ton standard coal equivalent). Further, a is a coefficient of the model, b, c, d and e are the indexes of the corresponding variables and f represents the error. This means that a 1% change rate of P, A, T and S will lead to a b%, c%, d% and e% change in I, respectively [13,25].

2.4. Calculation Formula of Tapio Decoupling Elasticity Index

Decoupling theory first appeared in the field of physics, and is used to indicate that two or more physical variables no longer respond according to the inherent path. Tapio put forward the concept of the decoupling index, which is used to indicate CO₂ emissions status [36]. The calculation formula of the decoupling index is as follows:

$$\mathrm{e}=\frac{\mathsf{\Delta }{\mathrm{CO}}_2/{\mathrm{CO}}_2}{\mathsf{\Delta }\mathrm{GDP}/\mathrm{GDP}}$$

(5)

where △CO₂/CO₂ and △GDP/GDP represent the growth rate of CO₂ emissions and GDP growth rate, respectively. Tapio divided the relationship between CO₂ emissions and economic growth into three states: connected, decoupling and negative decoupling and subdivided them into eight states based on economic development and index e [37], which is shown in

Table 1. Decoupling index and decoupling status.

Economic	△CO2/CO2	△GDP/GDP	Decoupling Index	Decoupling Status
Economic Growth	−	+	e < 0	Strong decoupling state
	+	+	0 ≤ e < 0.8	Weak decoupling state
	+	+	0.8 ≤ e < 1.2	Expansion connection state
	+	+	e ≥ 1.2	Expansion negative decoupling state
Economic Recessions	+	−	e < 0	Strong negative decoupling state
	−	−	0 ≤ e < 0.8	Weak negative decoupling state
	−	−	0.8 ≤ e < 1.2	Recession connection state
	−	−	e ≥ 1.2	Recession decoupling state

.

3. Analysis of CO₂ Emissions

3.1. Change in GDP and CO₂ Emissions

As shown in Figure 1a, the GDP of the Yangtze River Delta increased from CNY 3409.5 billion in 2001 to CNY 20,283.4 billion in 2019, increasing by 494.91%. With the development of the economy and the increase in energy consumption, CO₂ emissions increased from 584.06 Mt in 2001 to 1786.98 Mt in 2019, increasing by 205.96% (Figure 1b). Generally, the economic growth rate was faster than that of CO₂ emissions. From 2001 to 2011, the GDP growth of the Yangtze River Delta was relatively fast, with an annual rate of 12.46%, and the economic development has slowed down, with a rate of 7.92%, since 2012. CO₂ emissions showed a quick increase from 2001 to 2013, with an annual rate of 9.16% and an obvious slow growth trend, with an annual rate of 1.3%, since 2013. A series of policy and technology measures have been implemented to reduce CO₂ emissions in the Yangtze River Delta. Energy efficiency has been going through huge changes, with the improvement of technological innovation ability, equipment development and the supply structure’s optimization. As a result, energy intensity decreased, and then the CO₂ emissions growth rate declined [38,39].

Among all provincial administrative regions in the Yangtze River Delta, Jiangsu Province had the largest GDP and highest CO₂ emissions, accounting for 41.52% ± 2.57% and 40.78% ± 3.46% of the total, respectively, followed by Zhejiang Province, at 27.67% ± 1.60% and 24.59% ± 1.93%. The proportion of GDP of Shanghai and Anhui Provinces was 18.17% ± 1.45% and 13.79% ± 0.90%, respectively, and that of CO₂ emissions was 14.41% ± 3.17% and 20.23% ± 1.6%, respectively. The contribution of Shanghai and Zhejiang Province to the regional GDP was 3.76% and 3.08% higher than that of CO₂, respectively, whereas that of Anhui Province was 6.44% lower. The contribution data indicate that Shanghai and Zhejiang Province had higher economic development quality.

In Figure 2a, the CO₂ emissions intensity of the three provinces and one city in the region presented an overall downward trend between 2010 and 2019. The CO₂ emissions intensity of Shanghai was the lowest, dropping from 1.84 tonnes CO₂/CNY 10⁴ in 2001 to 0.58 tonnes CO₂/CNY 10⁴ in 2019, a reduction of 68.48%. The CO₂ emissions intensity of Anhui Province decreased from 2.69 tonnes CO₂/CNY 10⁴ in 2001 to 1.36 tonnes CO₂/CNY 10⁴ in 2019, and the average value was the highest in the Yangtze River Delta, 1.93 times that of Shanghai. The CO₂ emissions intensity of Zhejiang Province and Jiangsu Province was between that of Shanghai and Anhui Province. CO₂ emissions intensity is affected by various factors such as scientific and technological progress, urbanization level and industrial structure. As a typical representative city of China, Shanghai has experienced rapid urbanization development, and the urbanization rate increased from 75.2% in 2001 to 88.30% in 2019 [40]. The proportion of tertiary industries such as finance and information and technology services continuously increased from 52.6% in 2001 to 72.9% in 2019 (Figure 2b). The tertiary industry has developed rapidly with a high proportion and low energy dependence, leading to low CO₂ emissions intensity [41]. Anhui Province had the lowest level of urbanization in the Yangtze River Delta, with an urbanization rate of only 54.7% in 2019. Additionally, the tertiary industry increased from 35.9% in 2001 to 41.3% in 2019. The tertiary industry of Anhui Province developed slowly and accounted for a lower proportion, and secondary industries such as high-energy-consuming industries accounted for a higher proportion compared with Shanghai [42]. The urbanization level and the proportion of tertiary industry in Zhejiang Province and Jiangsu Province were between those of Shanghai and Anhui Province, and the CO₂ emissions intensity was also between both. There is a large development imbalance in the Yangtze River Delta, resulting in large differences in CO₂ emissions intensity. Therefore, deepening the economic integration of the Yangtze River Delta, strengthening cooperation between the economy, science and technology, ecology and other aspects and finally forming a green and low-carbon regional development model is conducive to reducing carbon emissions intensity in the future.

3.2. Analysis of CO₂ Decoupling

As shown in

Table 2. Decoupling state of CO₂ emissions in the Yangtze River Delta.

Area	Year	ΔCO2/CO2	ΔGDP/GDP	e	State
Shanghai City	2000–2005	0.30	0.58	0.52	Weak decoupling state
	2005–2010	0.23	0.70	0.33	Weak decoupling state
	2010–2015	−0.001	0.44	−0.002	Strong decoupling state
	2015–2019	−0.012	0.29	−0.042	Strong decoupling state
Zhejiang Province	2000–2005	0.79	0.65	1.22	Expansion negative decoupling state
	2005–2010	0.41	0.75	0.55	Weak decoupling state
	2010–2015	0.049	0.48	0.10	Weak decoupling state
	2015–2019	0.007	0.33	0.02	Weak decoupling state
Jiangsu Province	2000–2005	1.07	0.66	1.62	Expansion negative decoupling state
	2005–2010	0.49	0.88	0.56	Weak decoupling state
	2010–2015	0.22	0.58	0.38	Weak decoupling state
	2015–2019	0.11	0.31	0.35	Weak decoupling state
Anhui Province	2000–2005	0.23	0.50	0.47	Weak decoupling state
	2005–2010	0.73	0.87	0.84	Expansion negative decoupling state
	2010–2015	0.34	0.65	0.52	Weak decoupling state
	2015–2019	0.12	0.37	0.33	Weak decoupling state
The Yangtze River Delta	2000–2005	0.66	0.62	1.06	Expansion negative decoupling state
	2005–2010	0.47	0.81	0.57	Weak decoupling state
	2010–2015	0.17	0.54	0.32	Weak decoupling state
	2015–2019	0.08	0.32	0.24	Weak decoupling state

, the relationship between CO₂ emissions and economic growth was different among provincial areas in the Yangtze River Delta at different periods. The increase rate of CO₂ emissions in Shanghai was lower than that of the economy and was in a state of weak decoupling from 2001 to 2010. CO₂ emissions showed negative growth, and the economy has continued to grow since 2010. Therefore, the relationship between CO₂ emissions and the economy entered into a state of strong decoupling. Zhejiang Province and Jiangsu Province showed consistent change trends, and the increase rate of CO₂ emissions was larger than that of economic development during the period of 2000~2005, being in an expansion negative decoupling state. After 2005, the CO₂ emissions growth rate of both provinces was slower than the economic growth rate, and the e-value showed a downward trend, which indicated the relationship was in a state of weak decoupling. From 2005 to 2010, the increase rate of CO₂ emissions in Anhui Province was greater than that of economic development, being in an expansion negative decoupling status and a weak decoupling status at other times. Shanghai experienced rapid economic development between 2001 and 2009, and the total energy consumption growth rate was relatively high [43], resulting in a rapid rise in CO₂ emissions (Figure 1b). However, CO₂ emissions intensity continued to decrease with the development of science and technology and the optimization of energy structure and industrial structure, resulting in a slow rise in CO₂ emissions (Figure 1b). After reaching a peak of 207.6 Mt in 2013, Shanghai’s CO₂ emissions slowly fell back down and stabilized at approximately 195 Mt (Figure 1b), showing a significant sign of the decoupling state. Jiangsu Province, Zhejiang Province and Anhui Province were still in a state of CO₂ emissions growth due to the high proportion of industrial energy-consuming enterprises [44,45]. Large total energy consumption, high carbon emissions intensity and weak decoupling in these three provinces also led to the weak decoupling of CO₂ emissions in the entire region.

3.3. Characteristics of Influencing Factors on CO₂ Emissions

3.3.1. Population

As shown in Figure 3a, the total resident population of the Yangtze River Delta increased from 198.52 million in 2001 to 234.17 million in 2019, growing steadily with an average annual growth rate of 0.92%. Jiangsu Province had the largest population, accounting for 36.30~37.07% of the total population in the Yangtze River Delta, followed by Anhui Province, accounting for 26.11~30.87%. The population proportion of both provinces experienced a downward trend in the region, and that of Anhui Province decreased significantly. The population proportion in Zhejiang Province and Shanghai was 23.66~26.96% and 8.40~10.92%, respectively, with an upward trend. The annual population growth rate of Shanghai was the highest, with an average annual growth of 2.24%, followed by Zhejiang Province, with an average annual growth of 1.70% and Jiangsu Province, which had an average annual growth rate of 0.78%. Anhui Province had the lowest average annual population growth at approximately 0.30%, and some years even had negative growth. The rapid economic development in Shanghai and Zhejiang Province has attracted a large amount of talent and a large labor force from other provinces and cities such as Anhui Province and Jiangsu Province [46], so the population experienced rapid growth accordingly.

3.3.2. GDP Per Capita

As shown in Figure 3b, the GDP per capita of Shanghai was the highest, increasing from CNY 40,552 in 2001 to CNY 137,312 in 2019, with approximately a 3.39-times increase and a 7% average annual growth rate. The GDP per capita of Anhui Province was the lowest, accounting for only 17.57~32.30% of Shanghai’s. However, it increased the fastest, with approximately a 6.23-times increase and a 10.7% annual rate. The GDP per capita and change rate of Jiangsu Province and Zhejiang Province were between Shanghai and Anhui Province’s. As an economic and administrative center in the Yangtze River Delta, Shanghai has a superior geographical location, convenient transportation infrastructure, developed science and technology and sustained and rapid economic growth, which has driven the economic development of the surrounding areas [47]. Zhejiang Province and Jiangsu Province are adjacent to Shanghai and are strongly affected by its economic development. Moreover, the Hangzhou and Ningbo metropolitan areas in Zhejiang Province and the Nanjing and Suzhou metropolitan areas in Jiangsu Province have all formed a high level of economic development [48]. Meanwhile, Anhui Province is the farthest from Shanghai among the three provinces and the least affected. Overall, the economic development level of its provincial capital city, Hefei metropolitan area, is relatively weak and needs to be further strengthened compared with the above four metropolitan areas in the region [49].

3.3.3. Energy Intensity

Energy intensity refers to the consumption of standard coal equivalent (CE) per CNY 10⁴ of GDP. As shown in Figure 3c, energy intensity generally showed a downward trend. Shanghai had the lowest energy intensity, decreasing from 0.83 tonnes of CE/CNY 10⁴ in 2001 to 0.36 tonnes of CE/CNY 10⁴ in 2019, decreasing by 56.63%. Anhui Province had the highest energy intensity, decreasing from 1.10 tonnes CE/CNY 10⁴ in 2001 to 0.46 tonnes CE/CNY 10⁴ in 2019, decreasing by 58.18%. Zhejiang and Jiangsu Provinces’ energy intensity was between that of Anhui Province and Shanghai. Energy intensity reflects the dependence of economic development on energy consumption, affected by technological development and economic structure. The lower the energy intensity, the less energy consumed and the higher the economic development quality. Compared with the other three provinces, Shanghai is in the lead regarding its technology and service sector. Therefore, the energy use for economic growth is low [43,47].

3.3.4. Energy Structure

Different energy structures will lead to a change in CO₂ emissions, which is one of the driving factors affecting CO₂ emissions [50]. The energy structure can be expressed by the ratio of CO₂ emissions to consumed standard coal equivalent. The higher the value is, the higher the proportion of fossil energy with low calorific value such as coal in the energy structure and the larger the CO₂ emissions [51,52].

As shown in Figure 3d, the general trend of energy structure in Shanghai and Zhejiang Province declined with slight fluctuation. The energy structure decreased from 2.18 and 1.78 tonnes of CO₂ emissions per tonne of CE in 2001 to 1.62 and 1.53 tonnes of CO₂ emissions per tonne of CE in 2019, respectively. The proportion of coal consumption decreased in Shanghai and Zhejiang Province in 2019, which accounted for 31.0% and 40.1%, respectively. Meanwhile, the percentage of non-fossil fuels is improving, and the energy structure is continuously being optimized. The energy structure of Anhui Province and Jiangsu Province fluctuated largely and was still high, with 2.94 and 2.47 tonnes of CO₂ emissions per tonne of CE, respectively, in 2019. Although the proportion of coal in the energy structure decreased in these two provinces, it still accounted for 55.0% and 69.8%, respectively, in 2019, which was high compared with Shanghai and Zhejiang Province, and the energy structure needs to be further improved [53,54].

4. Discussion and Prediction of the Model Equation of CO₂ Emissions

4.1. Analysis of the Influencing Factors of CO₂ Emissions

To avoid possible multicollinearity between variables, the ridge regression methods were used based on the CO₂ emissions (dependent variable) and their affecting factors such as population, GDP per capita, energy intensity and energy structure (independent variable) [13,22]. When the ridge regression coefficient K is 0.01, the ridge track map changes gradually and smoothly. The ridge regression results are shown in

Table 3. The model equation of CO₂ emissions.

Variable	Shanghai City	Zhejiang Province	Jiangsu Province	Anhui Province
Constant term	−8.102 **	−8.618 **	−35.091 **	−8.581 *
LnP	1.315 **	0.600 **	3.696 **	0.330 *
LnA	0.292 **	0.807 **	0.767 **	1.094 **
LnT	0.467 **	0.465 **	1.009 **	1.150 **
LnS	0.229 *	1.248 **	0.785 **	0.928 **
R-Squared	0.983	0.994	0.998	0.998
Adjust R-Squared	0.978	0.992	0.998	0.997
F value	200.02	564.07	2134.87	1654.02
Sig F	0.000	0.000	0.000	0.000

Note: * p < 0.05, ** p < 0.01.

. All variables pass the 5% significance level test, and most variables reach the significance level of 1%. The R² was between 0.983 and 0.998, and the F statistic passed the significance level test of 1%. The provincial data for population, GDP per capita, energy intensity and structure from 2001 to 2019 were inserted into the corresponding model equation to calculate the simulated value. The simulated value and actual value were analyzed by the means of regression analysis. The R² is between 0.9834 and 0.9982, indicating that the prediction accuracy of each model equation is high.

The population, per capita GDP, energy intensity and energy structure positively affect CO₂ emissions based on the model equation coefficient. Each factor has different effects on CO₂ emissions in different provinces and the city. The increase in population has a greater impact on the CO₂ emissions in Shanghai and Jiangsu Province. The CO₂ emissions of Shanghai and Jiangsu Province increase by 1.315% and 3.696%, respectively, when the population increases by 1%. GDP per capita has the most significant impact on Anhui Province. CO₂ emissions will increase by 1.094% with a 1% per capita GDP increase. The energy intensity has a greater impact on the CO₂ emissions of Jiangsu and Anhui Provinces, and it increases by 1% with a 1.009% and 1.150% CO₂ emissions increase, respectively. The energy structure has a large effect on the CO₂ emissions of Zhejiang Province. CO₂ emissions will increase by 1.248% with a 1% energy structure increase. The Yangtze River Delta is a developed region, attracting a large amount of technical talent and a high number of migrant workers. Rapid population growth, economic development, high urbanization level and high levels of energy consumption have led to an increase in CO₂ emissions. However, there are differences in the influence degree of various factors on CO₂ emissions among Shanghai, Jiangsu Province, Zhejiang Province and Anhui Province, caused by the imbalance in population growth and economic, technological and industrial development [44,55].

4.2. Prediction of CO₂ Emissions

The change rate of population, GDP per capita, energy intensity and energy structure are estimated based on the provincial panel data in the Yangtze River Delta from 2001 to 2019 combined with the 14th five-year planned development and medium–long-term planned development for 2021–2035. Then, nine development scenarios are set out, as shown in

Table 4. Development scenarios in the Yangtze River Delta.

Scenarios	Development Mode	GDP Growth	Technology Improvement
Scenario 1	High GDP growth–low carbon	High growth	High improvement
Scenario 2	High GDP growth–medium carbon	High growth	Medium improvement
Scenario 3	High GDP growth–high carbon	High growth	Low improvement
Scenario 4	Medium GDP growth–low carbon	Medium growth	High improvement
Scenario 5	Medium GDP growth–medium carbon	Medium growth	Medium improvement
Scenario 6	Medium GDP growth–high carbon	Medium growth	Low improvement
Scenario 7	Low GDP growth–low carbon	Low growth	High improvement
Scenario 8	Low GDP growth–medium carbon	Low growth	Medium improvement
Scenario 9	Low GDP growth–high carbon	Low growth	Low improvement

.

The population growth rate is set based on the annual change rate of the provincial panel data in the Yangtze River Delta from 2000 to 2019, and the population will reach a peak in 2028 and then decline.

The growth rate of GDP per capita is set based on the provincial economic development plan in the Yangtze River Delta. The annual growth rate of GDP per capita of Shanghai is 5.5%, and that of the other three provinces is 5.0%, taking 2020 as the benchmark. Under the high, medium and low GDP growth modes, the growth rate decreases by 0.45%, 0.5% and 0.55%, respectively, every five years in 2021~2040.

Technology improvement may reduce the energy intensity and optimize the energy structure. The average annual decline rate of energy intensity and energy structure is calculated according to the constraint value in the 14th 5-year plan of all provincial administrative regions in the Yangtze River Delta, and the rate is taken as the medium-carbon emissions mode in 2021~2040. The rate of the low-carbon emissions mode is reduced by 0.2%, and that of the high-carbon emissions mode is increased by 0.2% compared with the medium mode.

The CO₂ emissions of Shanghai, Zhejiang Province, Jiangsu Province and Anhui Province in the Yangtze River Delta in 2020~2040 are predicted by the regression models in Table 3 according to the above development scenarios. The calculation results are shown in Figure 4 and

Table 5. Peak amount and peak year of CO₂ emissions under the different development scenarios.

Scenario	Peak Quantities (Mt)				Peak Time (Year)
	Zhejiang Province	Jiangsu Province	Anhui Province	Yangtze River Delta	Zhejiang Province	Jiangsu Province	Anhui Province	Yangtze River Delta
Scenario 1	407.40	876.48	439.48	1908.25	2030	2029	2030	2029
Scenario 2	425.56	912.71	458.42	1982.13	2033	2031	2033	2031
Scenario 3	448.51	941.79	486.95	2062.74	2035	2032	2037	2034
Scenario 4	405.60	873.46	436.98	1901.35	2030	2029	2029	2029
Scenario 5	422.71	908.09	454.14	1971.61	2032	2031	2032	2031
Scenario 6	444.19	936.17	479.80	2047.49	2035	2032	2035	2033
Scenario 7	404.08	870.75	434.87	1895.78	2029	2028	2028	2028
Scenario 8	420.13	904.04	450.59	1962.02	2031	2030	2031	2030
Scenario 9	440.42	930.65	473.94	2033.77	2034	2031	2034	2032

.

The economy of Shanghai has entered into the late stage of industrialization with developed science and technology, rapid economic development and a high proportion of tertiary industry [56,57]. The quantity of CO₂ emissions reduces with economic growth, meaning they are strongly decoupled (Table 2). The quantity of CO₂ emissions in Shanghai will decrease under the eight development scenarios, which further proves the strong decoupling state. Only under the most unfavorable high GDP growth–high carbon emissions scenario will CO₂ emissions quantity rise slightly, reach a peak in 2025 and then decline (Figure 4a).

CO₂ emissions and economic development are still in a weak decoupled state in the three provinces, which means that CO₂ emissions continue to increase with economic growth (Table 2 and Figure 4b–d). However, the energy and industrial structure will be continuously optimized due to the improvement of science and technology and the guidance of national policies in future development [58,59]. The energy intensity and CO₂ emissions intensity will continue to decline, leading to CO₂ emissions declining after peaking in the three provinces (Figure 4b–d).

The CO₂ emissions will achieve a peak by 2030 with low-carbon-emissions technology under the different speeds of economic development in Zhejiang Province, Jiangsu Province and Anhui Province. Furthermore, the peak amount will be 404.08~407.40 Mt, 870.75~876.48 Mt and 434.87~439.48 Mt, respectively. The CO₂ emissions in the Yangtze River Delta will achieve a peak of 1895.78~1908.25 Mt in 2030.

The CO₂ emissions peak time in the three provinces will be 2031~2033 with medium-carbon-emissions technology under the three economic development scenarios, and the peak amounts are 420.13~425.56 Mt, 904.04~912.71 Mt and 450.59~458.42 Mt, respectively, representing an increase of an average of 4.22%, 3.97% and 3.95%, respectively, compared with the low-carbon-emissions technology. The Yangtze River Delta will reach a CO₂ emissions peak of 1962.02~1982.13 Mt in 2030~2031, representing an average increase of 3.69% compared with the low-carbon-emissions technology.

The CO₂ emissions peak time in the three provinces will generally be 2032~2035 with the high-carbon-emissions technology under the three economic development scenarios. The CO₂ emissions will even peak as late as 2037 under the high GDP growth and high CO₂ emissions scenario in Anhui Province. Under the high-carbon-emissions technology scenario, the peak carbon quantity in the three provinces will increase compared with that under the low-carbon-emissions technology scenario, with an average increase of 8.99%, 6.88% and 8.98%, respectively. Therefore, the CO₂ emissions peak time in the Yangtze River Delta will be postponed to 2032~2034, and the peak amount will reach 2033.77~2062.74 Mt, an average increase of 7.68% compared with low-carbon-emissions technology.

In short, low-carbon-emissions technology is conducive to achieving a CO₂ emissions peak in the Yangtze River Delta. In addition to reducing energy intensity and optimizing energy structure, carbon capture, utilization and storage (CCUS) is a frontier technology for decarbonization and carbon reduction [60,61]. CCUS technology breakthroughs in the future will play a greater role in achieving the goals of carbon peak and carbon neutrality [62,63].

5. Conclusions and Policy Recommendations

5.1. Conclusions

This work conducted a study on the decoupling relationship between CO₂ emissions and economic growth and the influencing factors of CO₂ emissions in the Yangtze River Delta from 2001 to 2019 based on the Tapio decoupling model and the extended STIRPAT model, respectively. Nine different development scenarios were set out to predict the peak amount and time of CO₂ emissions in the Yangtze River Delta. The main conclusions are as follows:

(1)

The GDP and CO₂ emissions in the Yangtze River Delta had an overall upward trend from 2001 to 2019. The growth rate of CO₂ emissions slowed down and began to be lower than the economic growth after 2005, and CO₂ emissions and economic growth gradually showed a trend of weak decoupling. Furthermore, the CO₂ emissions trends of different areas in the Yangtze River Delta differed. Shanghai experienced fluctuations and declined after reaching its peak of CO₂ emissions in 2013, entering a strong decoupling state from a weak decoupling state. The CO₂ emissions and economic growth in Zhejiang Province, Jiangsu Province and Anhui Province had been increasing, and the relationship entered into a state of weak decoupling from an un-decoupling state after 2005.

(2)

There is a significant positive correlation between CO₂ emissions and population, GDP per capita, energy intensity and energy structure. These factors have different effects on CO₂ emissions in different areas. The population and energy structure significantly impact the CO₂ emissions of Shanghai and Zhejiang Province, respectively. Population size and energy intensity have a great impact on Jiangsu Province, and the GDP per capita and energy intensity have a great impact on CO₂ emissions in Anhui Province. For each 1% change in these factors, the CO₂ emissions of the corresponding provinces and city increase by more than 1%. The difference in these influencing factors is mainly caused by the imbalance in development between the provinces and city in the Yangtze River Delta.

(3)

The peak amount and peak time of CO₂ emissions are different in the Yangtze River Delta under different development scenarios. The CO₂ emissions peak can be achieved by 2030 at 1895.78~1908.25 Mt under low-carbon-emissions technology. Compared with the low-carbon-emissions scenarios, both the medium- and high-carbon-emissions scenarios are not conducive to achieving carbon peak, with a 2~5-year delay in peak time and an increased emissions amount by 3.69~7.68%. The peak time of CO₂ emissions varies among all provincial administrative regions in the Yangtze River Delta. Shanghai will achieve its carbon peak time by 2025 at the latest, while Jiangsu Province, Zhejiang Province and Anhui Province will generally achieve it in 2030–2035.

5.2. Policy Recommendations

Developing and applying low-carbon technology is the key to achieving the regional carbon peak. The policy recommendations are as follows.

(1)

At present, industrial energy consumption accounts for approximately 70% of the total terminal energy consumption in the Yangtze River Delta. Industries with high energy consumption and emissions, such as the steel, thermal power and petrochemical industries, account for a high proportion. Moreover, the economic development of Jiangsu Province and Anhui Province deeply relies on these high-emissions industries. To reduce CO₂ emissions, the high-energy consuming industries in the Yangtze River Delta need to be upgraded to become refined and green, and the industrial energy consumption will further decline to approximately 45% of the total in 2060.

(2)

The government should optimize the energy structure and accelerate the use of non-fossil energies such as wind energy, solar energy, nuclear energy and biomass energy. The Yangtze River Delta is located near the coast, with developed agriculture and abundant offshore wind energy resources and biomass resources. Photovoltaic enterprises are agglomerated. In recent years, the utilization of non-fossil energy has been accelerated in the region. As a result, wind power generation, biomass energy utilization and solar power generation in this region rank at the forefront in China. There are several operating nuclear power plants in Jiangsu Province and Zhejiang Province, respectively. The share of nuclear power generation in Zhejiang Province is 12% of the province’s total electricity consumption, and it will increase with nuclear power development. Non-fossil energy shows a rapid upward trend, and its proportion is expected to gradually increase from 16% in 2020 to 30% in 2030 and 86% in 2060.

(3)

Energy consumption in construction and transportation is expected to continue to increase in the Yangtze River Delta in the future. Therefore, it is necessary to promote the green public transport system and new energy vehicles, pay attention to the whole process of building energy conservation and improve the level of green building management. In addition, the local government should also actively implement environmental education to increase awareness of low-carbon lifestyles and encourage people to participate in low-carbon activities.