Decomposition Analysis and Trend Prediction of Energy-Consumption CO₂ Emissions in China’s Yangtze River Delta Region

Abstract

This study calculated CO₂ emissions related to the consumption of primary energy by five sectors in the Yangtze River Delta region over 2000 to 2019. The Logarithmic Mean Divisia Index (LMDI) decomposition method was used to establish the factor decomposition model of CO₂ emissions change. The LMDI model was modified to assess the impact of five influencing factors, namely energy structure, energy intensity, industrial structure, economic output, and population size, on CO₂ emissions in the Yangtze River Delta region over the study period. The empirical results show that economic output has the largest positive effect on the growth in CO₂ emissions. Population size is the second most important factor promoting the growth in CO₂ emissions. Energy intensity is the most inhibitory factor to restrain CO₂ emissions, with a significant negative effect. Energy structure and industrial structure contribute insignificantly to CO₂ emissions. Using data on CO₂ emissions in the Yangtze River Delta region from 2000 to 2019, the GM (1, 1) model was applied for future forecasts of primary energy consumption and CO₂ emissions. Specific policy suggestions to mitigate CO₂ emissions in Yangtze River Delta region are provided.

1. Introduction

Climate change caused by greenhouse gas emissions poses an ever-increasing threat to social development and human health; therefore, lowering carbon dioxide (CO₂) emissions has emerged as a critical international concern. The main source of CO₂ emissions is energy consumption, and many scholars have conducted related research on energy consumption in human life and production activities [1,2,3,4]. In 2019, China’s yearly greenhouse gas emissions amounted to 27% of global emissions, surpassing the total emissions of Organization for Economic Cooperation and Development (OECD) countries for the first time [5], and in 2021 it consumed 26.49% of world primary energy and created 31.06% of global CO₂ emissions [6]. The country’s latest Five-Year Plan includes a set of regional emission reduction targets to control CO₂ emissions. As is commonly known, the greenhouse effect is directly caused by the large amounts of greenhouse gas emissions—mainly comprising CO₂. Effective control of CO₂ emissions is therefore imperative to mitigating global warming.

China, as the world’s largest developing country, continues to prioritize economic development, particularly regional economic development. Its most developed region, the Yangtze River Delta region, holds a crucial strategic position and is a driving force in modernizing China’s economy and society. As seen in Figure 1, it comprises Jiangsu and Zhejiang provinces, centered on Shanghai, is a region of rapid industrialization and has China’s densest population, highest urbanization, and most developed economy.

According to the China Statistical Yearbook [7], gross domestic product (GDP) of the Yangtze River Delta region in 2021 was around 23.3 trillion yuan at the current price, accounting for nearly 20% of China’s national economy. The region’s GDP increased at a high average annual growth rate of 10% from 2000 to 2019, mainly accounted for by Jiangsu province with a rate of 6 trillion yuan in 2019, followed by Zhejiang Province and Shanghai. During the same period, the Yangtze River Delta region’s energy consumption increased from 200.71 million tons to 666.15 million tons of standard coal, representing nearly 15% of China’s total energy consumption. In particular, energy consumption in the region’s industry sector increased from 59.91 million tons of standard coal in 2000 to 130.63 million tons in 2019, accounting for the largest sectoral share. The Yangtze River Delta region’s CO₂ emissions rose considerably as a result of its usage of coal, the primary energy utilized, which has led to a number of environmental issues.

In recent years, global warming has become a major concern, and numerous studies have attempted, using various methodologies, to determine the factors that influence CO₂ emissions. The main methodologies used are the environmental Kuznets curve approach, STIRPAT, IPAT, and regression, and decomposition analysis. While each method has its advantages, that of decomposition analysis is its ability to quantitatively depict how driving forces influence changes based on an aggregation. As decomposition analysis methods, both structural decomposition analysis (SDA) and index decomposition analysis (IDA) are frequently employed; however, the IDA method is more widely used due to the availability of data [8]. In-depth investigations are not appropriate for SDA since it requires input-output data, which are typically released every five years in China—IDA simply utilizes departmental aggregate data, making it appropriate for time series modeling. The LMDI approach was first proposed by Ang and Choi [9], and is highly regarded as an IDA technique due to its theoretical base, adaptability, ease of usage, and result interpretation [8,10]. Compared with other IDA approaches, the LMDI method has several practical advantages in terms of its applicability, including perfect decomposition, the additive property of results and consistency in aggregation [10]. These advantages explain the wide use of LMDI in research over many fields, such as in energy supply and demand [11,12], and in energy-related gas emissions [13,14]. Decomposition studies have been conducted in numerous countries and regions around the world, and this study focuses on CO₂ emissions research in China. In addition to studying CO₂ emissions throughout whole China [15,16], many studies adopt sectoral and regional perspectives to identify the driving factors of CO₂ emissions. Studies relative to China have assessed CO₂ emissions at the sectoral level, specific to industry [17], construction [18], cement industry [19], chemical industry [20], petroleum refining and coking industry [21], transport [22,23], and manufacturing industry [24], identifying the driving factors for reducing CO₂ emissions by sector. In addition to sector-level studies, several scholars have investigated CO₂ emissions from a regional perspective [25,26,27,28,29].

Estimating and projecting CO₂ emissions into the atmosphere is critical for analyzing and planning mitigation activities, as well as establishing scenarios for future emissions. Deng [30] was the first to propose utilizing the grey model (GM) to analyze uncertainty and information deficiency. As a result of this concept, the grey model has been used in a range of complex applications [31,32,33,34,35].

Thus, in this study, we examined the factors influencing CO₂ emissions in the Yangtze River Delta region from 2000 to 2019 using the LMDI method, and the GM (1,1) model was applied to forecast primary energy consumption and CO₂ emissions in the short term. The goal of this study was to demonstrate the potential of each influencing factor in reducing CO₂ emissions by exploring the impact of each factor on CO₂ emissions changes and predict future trends in CO₂ emissions changes to explore potential future pathways for efficient CO₂ emissions and execute low-carbon development strategies. This novel study reveals the insight for investigating CO₂ emissions on production activities in the past and for future timepoints.

2. Materials and Methods

2.1. The LMDI Decomposition Method

The LMDI method is a popular decomposition technique in energy and environmental research. In terms of theoretical background, zero residual, adaptability, result interpretation, and other qualities, Ang [8] argued that LMDI was the most preferable decomposition method. Using the additive LMDI decomposition model, we incorporate qualitative and quantitative analysis in the research process to investigate the effects of different factors on CO₂ emissions in the Yangtze River Delta region.

The analysis process involves categorizing by industry sectors and energy types based on the extended Kaya decomposition method. Changes in CO₂ emissions may be broken down into emission coefficient effect, energy structure effect, energy intensity effect, industrial structure effect, economic output effect, and population size effect.

We decomposed the carbon emissions of the Yangtze River Delta region by the LMDI method from the following Kaya identity:

$$C=\frac{C}{E}·\frac{E}{GDP}·\frac{GDP}{P}·P$$

(1)

where $C$ denotes the total carbon emissions, $E$denotes the total energy consumption, $GDP$denotes gross domestic product and $P$ denotes population scale.

We then extended Kaya’s identity, as follows:

$$\mathit{C}={\displaystyle \sum }_{i,j}{\mathit{C}}_{\mathit{i},\mathit{j}}={\displaystyle \sum }_{\mathit{i},\mathit{j}}\frac{{\mathit{C}}_{\mathit{i},\mathit{j}}}{{\mathit{E}}_{\mathit{i},\mathit{j}}}·\frac{{\mathit{E}}_{\mathit{i},\mathit{j}}}{{\mathit{E}}_{\mathit{i}}}·\frac{{\mathit{E}}_{\mathit{i}}}{{\mathit{Q}}_{\mathit{i}}}·\frac{{\mathit{Q}}_{\mathit{i}}}{\mathit{Y}}·\frac{\mathit{Y}}{\mathit{P}}·\mathit{P}={\displaystyle \sum }_{\mathit{i},\mathit{j}}{\mathit{F}}_{\mathit{i},\mathit{j}} · {\mathit{S}}_{\mathit{i},\mathit{j}} · {\mathit{I}}_{\mathit{i}} · {\mathit{R}}_{\mathit{i}} · \mathit{G} · \mathit{P}$$

(2)

where $i$denotes sector, $j$ denotes energy type, $C_{i,j}$ denotes carbon emission from energy $j$consumption by sector $i$ (in units of 10,000 tons), $E_{i,j}$ denotes energy $j$consumption by sector $i$ (in units of 10,000 tons of standard coal), $E_i$ represents total energy consumption of sector $i$ (in units of 10,000 tons of standard coal), $Q_i$ represents value added of sector $i$(in units of 100 million yuan),$Y$ refers to$GDP$ (in units of 100 million yuan), and $P$ refers to resident population (in units of million people). $F_{i,j}=C_{i,j} /E_{i,j}$ denotes the carbon emission coefficient of energy $j$ in sector$i$ (the carbon emission coefficient factor); $S_{i,j}=E_{i,j} /E_i$ denotes the proportion of energy $j$ in sector $i$ (the energy structure factor); $I_i=E_i /Q_i$ denotes energy intensity of sector $i$ (the energy intensity factor); $R_i=Q_i / Y$ denotes sector $i$ value added proportion in $GDP$ (the industrial structure factor); $G=Y /P$ denotes economic output per capita in a period (the economic output factor); and $P$ denotes resident population (the population size factor).

Applying the LMDI additive method, the carbon emission factors can be decomposed as below:

$$\Delta C=C^t-C^0=\Delta C_F^t+\Delta C_S^t+\Delta C_I^t+\Delta C_R^t+\Delta C_G^t+\Delta C_P^t$$

(3)

The expressions for determining the decomposition factors’ contribution values are as follows:

Emission coefficient effect:

$$\Delta {\mathit{C}}_{\mathit{F}}^{\mathit{t}}={\displaystyle \sum }_{\mathit{i},\mathit{j}}\frac{{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathit{t}}-{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathbf{0}}}{\mathit{l}\mathit{n}{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathit{t}}-\mathit{l}\mathit{n}{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathbf{0}}}·\mathit{l}\mathit{n}\frac{{\mathit{F}}_{\mathit{i},\mathit{j}}^{\mathit{t}}}{{\mathit{F}}_{\mathit{i},\mathit{j}}^{\mathbf{0}}}$$

(4)

Energy structure effect:

$$\Delta {\mathit{C}}_{\mathit{S}}^{\mathit{t}}={\displaystyle \sum }_{\mathit{i},\mathit{j}}\frac{{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathit{t}}-{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathbf{0}}}{\mathit{l}\mathit{n}{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathit{t}}-\mathit{l}\mathit{n}{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathbf{0}}}·\mathit{l}\mathit{n}\frac{{\mathit{S}}_{\mathit{i},\mathit{j}}^{\mathit{t}}}{{\mathit{S}}_{\mathit{i},\mathit{j}}^{\mathbf{0}}}$$

(5)

Energy intensity effect:

$$\Delta {\mathit{C}}_{\mathit{I}}^{\mathit{t}}={\displaystyle \sum }_{\mathit{i},\mathit{j}}\frac{{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathit{t}}-{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathbf{0}}}{\mathit{l}\mathit{n}{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathit{t}}-\mathit{l}\mathit{n}{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathbf{0}}}·\mathit{l}\mathit{n}\frac{{\mathit{I}}_{\mathit{i}}^{\mathit{t}}}{{\mathit{I}}_{\mathit{i}}^{\mathbf{0}}}$$

(6)

Industrial structure effect:

$$\Delta {\mathit{C}}_{\mathit{R}}^{\mathit{t}}={\displaystyle \sum }_{\mathit{i},\mathit{j}}\frac{{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathit{t}}-{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathbf{0}}}{\mathit{l}\mathit{n}{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathit{t}}-\mathit{l}\mathit{n}{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathbf{0}}}·\mathit{l}\mathit{n}\frac{{\mathit{R}}_{\mathit{i}}^{\mathit{t}}}{{\mathit{R}}_{\mathit{i}}^{\mathbf{0}}}$$

(7)

Economic output effect:

$$\Delta {\mathit{C}}_{\mathit{G}}^{\mathit{t}}={\displaystyle \sum }_{\mathit{i},\mathit{j}}\frac{{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathit{t}}-{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathbf{0}}}{\mathit{l}\mathit{n}{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathit{t}}-\mathit{l}\mathit{n}{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathbf{0}}}·\mathit{l}\mathit{n}\frac{{\mathit{G}}^{\mathit{t}}}{{\mathit{G}}^{\mathbf{0}}}$$

(8)

Population size effect:

$$\Delta {\mathit{C}}_{\mathit{P}}^{\mathit{t}}={\displaystyle \sum }_{\mathit{i},\mathit{j}}\frac{{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathit{t}}-{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathbf{0}}}{\mathit{l}\mathit{n}{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathit{t}}-\mathit{l}\mathit{n}{\mathit{C}}_{\mathit{i},\mathit{j}}^{\mathbf{0}}}·\mathit{l}\mathit{n}\frac{{\mathit{P}}^{\mathit{t}}}{{\mathit{P}}^{\mathbf{0}}}$$

(9)

where $C^0$ and $C^t$ denote total CO₂ emissions of a region in period $0$ and period $t$, respectively. $\Delta C$ denotes the change in CO₂ emissions from period $0$ to period $t$. Accordingly, $\Delta C$ can be decomposed into the emission coefficient effect ($\Delta C_F$), energy structure effect ($\Delta C_S$), energy intensity effect ($\Delta C_I$), industrial structure effect ($\Delta C_R$), economic output effect ($\Delta C_G$) and population size effect ($\Delta C_P$).

As there is little change in the carbon emission coefficient of the same energy type stated in IPCC 2006 [36], in Equation (3), $\Delta C_F$ is therefore always equal to 0. Therefore, only the latter five factors need to be considered. Equation (3) can be simplified as below.

Total effect:

$$\Delta \mathit{C}={\mathit{C}}^{\mathit{t}}-{\mathit{C}}^{\mathbf{0}}=\Delta {\mathit{C}}_{\mathit{S}}^{\mathit{t}}+\Delta {\mathit{C}}_{\mathit{I}}^{\mathit{t}}+\Delta {\mathit{C}}_{\mathit{R}}^{\mathit{t}}+\Delta {\mathit{C}}_{\mathit{G}}^{\mathit{t}}+\Delta {\mathit{C}}_{\mathit{P}}^{\mathit{t}}$$

(10)

2.2. GM (1, 1) Model

Many scholars use different models to test the CO₂ emissions of various regions. Among the current numerous prediction methods, the grey prediction model has the advantages of simple modeling, use with limited samples, simple data requirements, convenient operation, and high practicability. Grey prediction is based on the grey dynamic model, which is a method enabling effective control over a system with insufficient data and incomplete information, thus is widely favored by many scholars. It is widely used across broad-ranging fields including industry, agriculture, economy and society, and has achieved good results. Moreover, much research on the theory of the model has been conducted to improve its test accuracy. In terms of the complex issues surrounding energy, these are usually accompanied by low information amount, few samples, and incomplete data, which makes grey prediction unique as it only has low data requirements to make accurate predictions. Therefore, it is reasonable to use the grey prediction GM (1, 1) model to make short-term predictions for the Yangtze River Delta region.

The construction of GM (1, 1) grey prediction model is as follows:

Original series $X^{\left(0\right)}$ is defined as:

$${\mathit{X}}^{\left(\mathbf{0}\right)}=\left\{{\mathit{x}}^{\left(\mathbf{0}\right)}\left(\mathbf{1}\right), {\mathit{x}}^{\left(\mathbf{0}\right)}\left(\mathbf{2}\right),\dots ,{\mathit{x}}^{\left(\mathbf{0}\right)}\left(\mathit{n}\right)\right\}$$

(11)

where n ≥ 3.

Get a new series $X^{\left(1\right)}$:

$${\mathit{X}}^{\left(\mathbf{1}\right)}=\left\{{\mathit{x}}^{\left(\mathbf{1}\right)}\left(\mathbf{1}\right), {\mathit{x}}^{\left(\mathbf{1}\right)}\left(\mathbf{2}\right),\dots ,{\mathit{x}}^{\left(\mathbf{1}\right)}\left(\mathit{n}\right)\right\}$$

(12)

where $x^{\left(1\right)}\left(n\right)={\displaystyle \sum }_{k=1}^n{x}^{\left(0\right)}\left(k\right)$.

The basic form of GM (1, 1) is:

$${\mathit{X}}^{\left(\mathbf{0}\right)}\left(\mathit{t}\right)+\mathit{\alpha }·{\mathit{X}}^{\left(\mathbf{1}\right)}\left(\mathit{t}\right)=\mathit{u}$$

(13)

where $t$ is the independent variable, $\alpha$is the developed coefficient, and $u$ is the grey controlled variable, which are estimated by the ordinary least-squares method. A grey prediction model can be constructed from $\alpha$ and $u$. $\alpha$ represents the developed law and trend of the sequence, and $u$ reflects the change relationship of the sequence.

A response equation can be generated using the estimated coefficients $\alpha$ and $u$:

$${\widehat{\mathit{x}}}^{\left(\mathbf{1}\right)}\left(\mathit{t}+\mathbf{1}\right)=\left({\mathit{x}}^{\left(\mathbf{0}\right)}\left(\mathbf{1}\right)-\frac{\mathit{u}}{\mathit{\alpha }}\right){\mathit{e}}^{-\mathit{\alpha }\mathit{t}}+\frac{\mathit{u}}{\mathit{\alpha }}$$

(14)

The prediction formula is expressed as:

$${\widehat{\mathit{x}}}^{\left(\mathbf{0}\right)}\left(\mathit{t}+\mathbf{1}\right)={\widehat{\mathit{x}}}^{\left(\mathbf{1}\right)}\left(\mathit{t}+\mathbf{1}\right)-{\widehat{\mathit{x}}}^{\left(\mathbf{1}\right)}\left(\mathit{t}\right)$$

(15)

The fitting and prediction results are discussed using the posterior variance test to show how reliable the GM (1, 1) model is. Posterior variance ratio $C$ and small error probability $P$ are both criteria for the posterior variance test.

The following is the definition of the posterior variance ratio $C$ and small error probability $P$:

$$\mathit{C}=\frac{{\mathit{S}}_{\mathbf{2}}}{{\mathit{S}}_{\mathbf{1}}}=\sqrt{\frac{{\mathit{S}}_{\mathbf{2}}{}^{\mathbf{2}}}{{\mathit{S}}_{\mathbf{1}}{}^{\mathbf{2}}}}$$

(16)

$$\mathit{P}=\left\{\left.\left|{\mathit{\epsilon }}^{\left(\mathbf{0}\right)}\left(\mathit{t}\right)-{\overline{\mathit{\epsilon }}}^{\left(\mathbf{0}\right)}\left(\mathit{t}\right)\right.\right|<\mathbf{0.6745}{\mathit{S}}_{\mathbf{1}}\right\}$$

(17)

where $C>0, 0\le P\le 1$, $S_2{}^2=\frac{1}{m-1}{{\displaystyle \sum }}_{t=1}^{m-1}{\left({\epsilon }^{\left(0\right)}\left(t\right)-{\overline{\epsilon }}^{\left(0\right)}\left(t\right)\right)}^2$, and $S_1{}^2=\frac{1}{m-1}{{\displaystyle \sum }}_{t=1}^m{\left(x^{\left(0\right)}\left(t\right)-{\overline{x}}^{\left(0\right)}\left(t\right)\right)}^2$. ${\epsilon }^{\left(0\right)}\left(t\right)$ is the residual between the predicted value ${\widehat{x}}^{\left(0\right)}\left(t\right)$ and the actual value $x^{\left(0\right)}\left(t\right)$ at time $t$. $P>0.95$ and $C<0.35$ indicates excellent prediction accuracy, $P>0.60$ and $C<0.80$ indicates qualified prediction accuracy, and $P<0.60$ and $C>0.80$ indicates unqualified prediction accuracy.

2.3. Data

All available data are from the study period, 2000–2019. Data on GDP, value added of each sub-sector and population were obtained from the China Statistical Yearbook (2001–2020) [7], Jiangsu Statistical Yearbook (2001–2020) [37], Zhejiang Statistical Yearbook (2001–2020) [38] and Shanghai Statistical Yearbook (2001–2020) [39] for the period 2000−2019. Data on the resident population is used as population data, while GDP and value-added of each sub-sector are converted into constant 2000 prices (base year is 2000) to remove the effects of price volatility. Energy consumption is the total amount of energy consumed based on eight energy sources: coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, and natural gas. Five sectors contribute to economic output: (1) agriculture, forestry, animal husbandry and fishery, (2) industry, (3) construction, (4) transport, storage and post, (5) wholesale, retail trade, hotels and catering services. The energy consumption data are total final consumption quantities, obtained from the Jiangsu Statistical Yearbook (2001−2020) [37], Zhejiang Statistical Yearbook (2001–2020) [38], Shanghai Statistical Yearbook (2001−2020) [39], and China Energy Statistical Yearbook (2001−2020) [40] for 2000−2019. The eight energy types involve quantities in different units, which are converted into standard coal consumption quantities in units of 10,000 tce (tce: tons of standard coal equivalent) using conversion coefficients (see

Table 1. Standard coal reference coefficient and carbon emission coefficient.

Energy Types	Standard Coal Reference Coefficient (kgce/kg)	Carbon Emission Coefficient (kg/kgce)
Coal	0.7143	0.7476
Coke	0.9714	0.8550
Crude oil	1.4286	0.5825
Gasoline	1.4714	0.5538
Kerosene	1.4714	0.5714
Diesel oil	1.4571	0.5921
Fuel oil	1.4286	0.6185
Natural gas a	1.3300	0.4435

Source: China Energy Statistical Yearbook [40] and IPCC 2006 [36].Notes: ^a: Standard coal reference coefficient of nature gas is in the range of 1.1000−1.3300 kgce/kg. Here, we chose 1.3300 kgce/kg based on the commonly used value for research.

). There are no direct CO₂ emissions figures available, and none are included in any statistical yearbook or government website. As a result, the majority of research on CO₂ emissions has calculated emissions using energy consumption. Therefore, we calculated amounts of provincial CO₂ emissions of the eight energy types in five sectors using Equation (18) as follows:

$$C={\displaystyle \sum }_j{C}_j={\displaystyle \sum }_j{E}_j·F_j$$

(18)

where $C$ refers to the total CO₂ emissions related to energy consumption; $j$ refers to eight energy types; $C_j$ is the CO₂ emissions of energy $j$; $E_j$ is the energy $j$consumption (standard coal equivalent); and $F_j$ represents the carbon emission coefficient of energy $j$. Table 1 shows the standard coal reference coefficient and carbon emission coefficient by energy type.

2.4. Limitations

Firstly, this study was limited to a focus on the CO₂ emissions research of the five major sectors related to production activities, exploring the emission reduction pathways in the Yangtze River Delta region from the perspective of the overall industry. Secondly, a short-term forecast was chosen as it could be more appropriately adapted to regional low carbon development. For further study, decomposition analysis and prediction of CO₂ emissions can be conducted and compared by sector.

3. Results and Discussion

3.1. Energy-Consumption CO₂ Emissions of Yangtze River Delta Region

In parallel with the rapid social and economic development of the Yangtze River Delta region, CO₂ emissions from energy consumption have increased. Figure 2 depicts the energy-consumption CO₂ emissions by sector for Jiangsu Province, Zhejiang Province, Shanghai, and the Yangtze River Delta region, during 2000–2019.

Figure 2d shows that CO₂ emissions in the Yangtze River Delta region increased rapidly before 2011, and then increased steadily during 2011–2019. Industrial CO₂ emissions have the largest share for the region, followed by the transport sector. Figure 2a,b show that industrial CO₂ emissions account for the largest proportion for Jiangsu and Zhejiang Provinces, tendencies of which varied over recent years. However, differing from Jiangsu and Zhejiang Provinces, Figure 2c depicts that since 2016, the proportion of CO₂ emissions from transport has exceeded that of industry in Shanghai. Industrial CO₂ emissions slowly declined from 2011, and the proportion of CO₂ emissions from transport has an upward trend.

As shown in Figure 3, energy-consumption CO₂ emissions in the Yangtze River Delta region show an increasing trend as a whole, from 56.51 million tons in 2000 to 139.05 million tons in 2019. This represents an average annual growth rate of 4.85%. Jiangsu Province contributed the largest amount of CO₂ emissions in the region.

3.2. Decomposition Analysis of CO₂ Emissions Factor

Using Equations (5)–(9), the driving factor effects on CO₂ emissions can be calculated, including the energy structure effect ($\Delta C_S$), energy intensity effect ($\Delta C_I$), industrial structure effect ($\Delta C_R$), economic output effect ($\Delta C_G$) and population size effect ($\Delta C_P$). The results of annual change and cumulative change in CO₂ emissions relative to 2000 are shown in

Table A1. Decomposition analysis of five factors on CO₂ emissions in the Yangtze River Delta region (10⁴ t) (AC = annual change, CC = cumulative change).

Time	$\mathbf{Energy} \mathbf{Structure} \Delta {\mathit{C}}_{\mathit{S}}^{\mathit{t}}$		$\mathbf{Energy} \mathbf{Intensity} \Delta {\mathit{C}}_{\mathit{I}}^{\mathit{t}}$		$\mathbf{Industrial} \mathbf{Structure} \Delta {\mathit{C}}_{\mathit{R}}^{\mathit{t}}$		$\mathbf{Economic} \mathbf{Output} \Delta {\mathit{C}}_{\mathit{G}}^{\mathit{t}}$		$\mathbf{Population} \mathbf{Size} \Delta {\mathit{C}}_{\mathit{P}}^{\mathit{t}}$		$\mathbf{Carbon} \mathbf{Emissions} \Delta {\mathit{C}}^{\mathit{t}}$
	AC	CC	AC	CC	AC	CC	AC	CC	AC	CC	AC	CC
2000	0	0	0	0	0	0	0	0	0	0	0	0
2001	−6.29	−6.29	−517.43	−517.43	0.02	0.02	515.49	515.49	45.98	45.98	37.77	37.77
2002	4.66	−1.63	−382.31	−899.74	1.40	1.42	610.48	1125.97	46.11	92.09	280.34	318.12
2003	−27.65	−29.28	−317.00	−1216.75	3.10	4.52	736.06	1862.04	54.43	146.52	448.94	767.05
2004	5.88	−23.40	992.71	−224.04	0.22	4.74	849.03	2711.07	84.67	231.19	1932.50	2699.55
2005	33.82	10.42	413.17	189.13	3.83	8.57	1038.69	3749.76	94.49	325.68	1583.99	4283.55
2006	20.46	30.88	−724.67	−535.54	3.22	11.79	1230.99	4980.75	121.98	447.65	651.99	4935.54
2007	−22.14	8.74	−669.59	−1205.13	−1.32	10.48	1370.27	6351.02	150.79	598.45	828.02	5763.56
2008	−19.22	−10.48	−629.10	−1834.23	2.81	13.28	1122.03	7473.05	116.84	715.29	593.35	6356.91
2009	22.47	11.99	−1005.80	−2840.04	3.92	17.20	1077.65	8550.70	120.38	835.67	218.62	6575.53
2010	3.75	15.75	−940.68	−3780.72	4.85	22.05	1243.00	9793.70	153.75	989.42	464.67	7040.19
2011	−3.21	12.54	−255.60	−4036.31	3.43	25.48	1131.92	10,925.62	94.57	1083.98	971.12	8011.31
2012	−66.29	−53.75	−1330.18	−5366.49	3.36	28.84	1095.75	12,021.37	64.87	1148.85	−232.49	7778.82
2013	−39.37	−93.12	−1270.88	−6637.37	2.14	30.98	1062.22	13,083.59	72.04	1220.89	−173.84	7604.97
2014	−22.60	−115.72	−714.37	−7351.74	2.24	33.22	974.90	14,058.49	55.81	1276.70	295.98	7900.95
2015	−55.41	−171.13	−552.31	−7904.05	3.14	36.36	1051.48	15,109.97	18.05	1294.75	464.95	8365.90
2016	14.40	−156.74	−1113.74	−9017.79	1.85	38.21	953.78	16,063.75	58.66	1353.42	−85.05	8280.85
2017	−53.51	−210.24	−1289.07	−10,306.85	1.71	39.91	904.44	16,968.19	68.00	1421.41	−368.43	7912.42
2018	−157.47	−367.72	−1121.95	−11,428.80	0.51	40.42	823.82	17,792.01	59.70	1481.12	−395.39	7517.04
2019	58.15	−309.57	−148.33	−11,577.14	1.62	42.04	770.56	18,562.57	54.71	1535.83	736.69	8253.73

in Appendix A.

The effects of economic output, population size, and energy intensity on CO₂ emissions are significant, as shown in Figure 4 and Figure 5, while the effects of energy structure and industrial structure are negligible. Economic output and population size have predominantly positive effects on the rise in CO₂ emissions, implying that changes in these factors caused the increase in CO₂ emissions. Economic output is the greatest pull factor on the increase in CO₂ emissions, and population size is the second most important factor promoting the growth in CO₂ emissions. Energy intensity has a negative effect on CO₂ emissions, and acts as an inhibitory factor, implying that the impact of changes in energy intensity lowered the CO₂ emissions decrease. Energy intensity is the most inhibitory factor to restrain CO₂ emissions, with a significant negative effect, other than for 2004 and 2005. Although energy structure had both positive and negative effects during the study period, the effect was negative overall. Industrial structure had a positive effect on CO₂ emissions except for 2007, although the pull effect was not significant.

As shown in Figure 6, owing to the Yangtze River Delta region’s large-scale economic boom from 2000 to 2019, cumulative CO₂ emissions increased by 18,562.57 × 10⁴ tons, and the economic output effect contributed the most to energy-consumption CO₂ emissions (i.e., up to 224.90%); additionally, population growth resulted in an increase in CO₂ emissions of 1535.83 × 10⁴ tons. Energy intensity led to a CO₂ emissions drop of 11,577.14 × 10⁴ tons. Energy structure adjustments resulted in a total decrease of 309.57 × 10⁴ tons of CO₂ emissions. Energy intensity, population size, and energy structure contributed −140.27%, 18.61%, and −3.75%, respectively. Industrial structure adjustments showed an insignificant positive effect on CO₂ emissions, with an increase of 42.04 × 10⁴ tons and a cumulative contribution ratio of 0.51%. It can be concluded, therefore, that the decisive factor leading to increased CO₂ emissions in the Yangtze River Delta region is economic output; population size is the second-highest driving factor leading to the rapid growth in CO₂ emissions, while reduction in energy intensity is the decisive factor in slowing the growth in CO₂ emissions, followed by energy structure. Industrial structure is not associated with the release of significant emissions.

3.2.1. Energy Structure Effect

Cumulative CO₂ emissions increased during 2000−2019, but energy structure had both positive and negative effects on CO₂ emissions, with an overall trend of lowering CO₂ emissions. In 2019, cumulative CO₂ emissions in the region were 8253.73 × 10⁴ tons, of which the contribution value of energy structure was −309.57 × 10⁴ tons, with a cumulative contribution rate of only 4%. Regarding the eight energy types considered in this study, coal has a carbon emission coefficient of 0.7476, but coke has a coefficient of 0.8550, which is higher. The increase in the coke proportion plays a significant role in CO₂ emissions change. Figure 7 shows that the proportion of coal consumption in the Yangtze River Delta region decreased from 49.78% in 2000 to 18.98% in 2019. However, the proportion of coke consumption increased from 13.15% in 2000 to 24.27% in 2019, and in 2018 it surpassed coal for the first time, indicating that coke acts as an alternative high-carbon energy type for coal. In addition, the proportion of low-carbon energy increased year by year. Although the proportion of low-carbon energy consumption has increased, energy structure is still dominated by coal and coke, which are high-carbon energy sources. While the annual contribution value of the energy structure effect varied significantly due to changes in the proportion of energy types, the contribution ratio of energy structure is relatively small compared to the annual and cumulative CO₂ emissions change. Therefore, it can be considered that the negative effect of energy structure on CO₂ emissions is not significant.

It should be noted that though the coal proportion has declined, the proportion of coke as a high-carbon energy increased each year. As a result, on the one hand, the energy structure has been optimized, which has stifled a rise in CO₂ emissions and had a negative impact. As a high-carbon energy source, however, coke consumption grew yearly. In general, therefore, the optimization of energy structure has not effectively curbed the rise in CO₂ emissions, reflecting a weak inhibition on CO₂ emissions. Currently, coal is the primary energy source in the region, while the proportion of solar, wind, and nuclear energy consumed is relatively minor. Therefore, it is essential to optimize the energy structure. This indicates that aggressive development of low-carbon energy sources such as solar, wind, nuclear, and other clean energies with low carbon emission coefficients is required, as well as the replacement of high-carbon energy sources with low-carbon energy sources and an increase in the supply of clean energy. Only by these measures can the energy structure procure a significantly inhibitive effect.

3.2.2. Energy Intensity Effect

Except for 2004 and 2005, the cumulative change in energy intensity in CO₂ emissions is shown to be −11,577.14104 tons (see Figure 4), indicating a negative effect on CO₂ emissions change in the region. Therefore, energy intensity is the most inhibitory factor, leading to a decrease in CO₂ emissions. In addition, energy intensity also shows a positive effect in some years, even though the value of its contribution to the reduction in CO₂ emissions is small.

As shown in Figure 8, energy intensity in the Yangtze River Delta region has demonstrated a general downward trend since 2000, achieving its lowest value in 2019; from 0.44 t standard coal/10⁴ yuan in 2000 to 0.19 t standard coal/10⁴ yuan in 2019, signifying a 42% drop. From 2000 to 2019, even though the energy intensity in the region generally showed a downward trend, it rebounded slightly from 2003 to 2005, showing a growth trend and leading to an increase in CO₂ emissions change. This could be because technological progress, on the one hand, improved energy efficiency and reduced energy demand, while on the other hand it fostered economic expansion and increased energy demand. These processes connected with these two findings cancel each other out, resulting in an insignificant positive effect on the change in CO₂ emissions from 2003 to 2005. Due to energy efficiency regulations and measures implemented between 2000 and 2002, energy intensity decreased significantly, producing an inhibitory effect of reducing CO₂ emissions. Due to technological innovation, the region’s energy intensity continued to decline after 2005, which explains why the reduction in CO₂ emissions is significantly impacted by energy intensity. In general, the change in energy intensity explains the considerable contribution to CO₂ emissions reduction, but also shows that there is still much room to improve CO₂ emissions reductions.

3.2.3. Industrial Structure Effect

Figure 4 reveals that excluding 2007, the industrial structure positively affected CO₂ emissions, although this effect was negligible during 2000−2019. This demonstrates that industrial structure is incapable of reducing CO₂ emissions effectively. China focused on industry structure reform and maintaining stable economic growth during the 13th Five-Year Plan (2016–2020), but how to effectively mitigate CO₂ emissions with economic development represents a challenging task. As a developed region, it is more difficult for industrial structure to exert an inhibitory effect on CO₂ emissions.

Table 2. Proportion of value added of sub-sector in Yangtze River Delta region during 2000−2019 (unit: %).

Year	Agriculture, Forestry, Animal Husbandry, and Fishery	Industry	Construction	Transport, Storage, and Post	Wholesale, Retail Trade, Hotels, and Catering Services
2000	9.11	44.49	5.60	6.66	11.11
2005	9.19	44.53	5.62	6.66	11.13
2010	9.33	44.57	5.67	6.66	11.10
2015	9.46	44.61	5.71	6.66	11.07
2019	9.48	44.63	5.71	6.66	11.08

demonstrates that the industrial sector remains the region’s major pillar sector, followed by the service sector, which includes retail commerce and hotels. However, the agriculture, transport, and construction sectors have relatively small shares. Furthermore, the proportion of value added of each sector remained nearly stable from 2000 to 2019, indicating that there was little change to the industrial structure during the study period, which explains the negligible impact of industrial structure on CO₂ emissions.

3.2.4. Economic Output Effect

Figure 4 and Figure 6 show that economic output in the Yangtze River Delta region is the key driving factor behind the rise in CO₂ emissions from 2000 to 2019—that is, it has a significantly positive effect on the growth in CO₂ emissions. The amount of energy consumption can reflect the regional economic situation to some extent, in terms of the amount needed to maintain steady economic activities, and as CO₂ emissions are the direct product of energy consumption, there is little doubt that CO₂ emissions and economic output are closely linked. China’s economy grew rapidly after the start, in 1978, of the period of reform and opening up. The region’s GDP grew from 1.95 trillion yuan in 2000 to 12.65 trillion yuan in 2019 at constant 2000 prices. GDP per capita increased from 14,534.90 yuan in 2000 to 81,420.03 yuan in 2019, with an average annual growth rate of 9%. Furthermore, the region’s primary energy consumption increased from 86.46 million tons of standard coal in 2000 to 227.87 million tons of standard coal in 2019—a 2.64-fold increase, which led to CO₂ emissions in 2019 reaching 139.05 million tons, 2.46 times the figure in 2000. This clarifies why GDP growth was the key factor in the exponential rise in CO₂ emissions. Therefore, maintaining a stable economic development rate and making adjustments to the economic transformation may reduce the dependence on energy demand and relieve the pull effect on CO₂ emissions.

3.2.5. Population Size Effect

According to Figure 4, population size has the second-highest positive impact on the growth in CO₂ emissions in the Yangtze River Delta region during the study period. The population grew on a yearly basis, and urbanization growth is significant, as a result of development of the economy and society. The residential population grew by 1% each year from 134.37 million in 2000 to 155.37 million in 2019. From 2000 to 2010, population size contributed increasingly to the change in CO₂ emissions; however, in recent years, the contribution value of population size has tended to remain stable, and the positive effect on CO₂ emissions has become limited. Figure 9 shows that the GDP per capita of the Yangtze River Delta region is much higher than that of China overall. Although the average annual growth rate of GDP per capita in Yangtze River Delta region is 9%, and that of China as a whole is 8%, economic development in the Yangtze River Delta region has far outpaced that of the nation as a whole. This, in consequence, led to a large population inflow, further accelerating population growth and the related consumer market of the region, which increased energy needs and the incentive to boost production, eventually leading to an increase in CO₂ emissions.

3.3. Forecasting Results

The grey prediction GM (1, 1) model was used to forecast future CO₂ emissions during 2020–2026 using the calculation results of CO₂ emissions over 2000–2019 as the original sequence. The prediction results and trend are shown in

Table 3. Prediction results of energy-consumption CO₂ emissions in 2020–2026 (million tons).

Year	2020	2021	2022	2023	2024	2025	2026
Predicted value	160.528	165.669	170.897	176.214	181.621	187.121	192.715

and Figure 10a, respectively.

Based on the calculation results of the GM (1, 1) model,

Table 4. GM (1, 1) model error test results of energy-consumption CO₂ emissions.

Year	Actual Value	Fitted Value	Residual	Relative Error (%)
2000	56.508	56.508	0	0
2001	56.886	77.698	−20.812	36.585
2002	59.69	81.426	−21.737	36.416
2003	64.179	85.218	−21.039	32.782
2004	83.504	89.075	−5.571	6.671
2005	99.344	92.997	6.347	6.389
2006	105.864	96.986	8.878	8.386
2007	114.144	101.043	13.101	11.478
2008	120.078	105.169	14.908	12.416
2009	122.264	109.366	12.898	10.549
2010	126.91	113.634	13.277	10.461
2011	136.622	117.975	18.647	13.649
2012	134.297	122.39	11.907	8.866
2013	132.558	126.88	5.679	4.284
2014	135.518	131.446	4.072	3.005
2015	140.167	136.091	4.077	2.908
2016	139.317	140.815	−1.498	1.075
2017	135.633	145.619	−9.986	7.363
2018	131.679	150.505	−18.826	14.297
2019	139.046	155.474	−16.429	11.815

shows that the average relative error of the GM (1, 1) model was 11.97%, implying the model had a good fit. The developed coefficient $\alpha$ is −0.017, grey controlled variable $u$ is 213.516, small error probability $P$ is 0.850, and posterior variance ratio $C$ is 0.197. As $C$< 0.35, $P$< 0.95, thus the forecast is deemed to demonstrate qualified accuracy, which indicates that the consequences were satisfactory for model construction. This model can therefore be utilized to predict CO₂ emissions in the region.

Based on Figure 10a, CO₂ emissions in the Yangtze River Delta region will rise from 2020 to 2026. If the economic policy, energy policy, and population policy of the region remain unchanged, CO₂ emissions from primary energy consumption will reach 192.715 million tons by 2026.

According to the energy conservation and emission reduction plan of the 14th Five-Year Plan (2021–2025), China set a target of 13.5% reduction in energy consumption per unit of GDP compared with the 2020 level. However, by 2025, the Yangtze River Delta region’s energy consumption per unit of GDP is extremely likely to be 11.6% lower than that in 2020, according to Figure 10b,c. This implies that relative to China’s general goal, the Yangtze River Delta region may be unable to meet the expected target.

At present, the Yangtze River Delta region is undergoing industrial development, which means much energy is consumed for the purposes of production and living. From Figure 10a, it can be clearly seen that the CO₂ emissions of primary energy consumption in the Yangtze River Delta region will increase yearly from 2020 to 2026. Although the local government is actively exploring the path of low-carbon economic development, and the measures taken to reduce emissions have also achieved certain results, based on the above results it can be clearly seen that CO₂ emissions will increase yearly in the future. Based on this study’s results, achieving the goal of mitigating CO₂ emissions in the short term therefore represents a substantial challenge. With reference to economic development of the Yangtze River Delta region, the region will only be able achieve CO₂ emissions reduction through the concerted efforts of the market, government, and individuals. From this it can be concluded sustainable economic development of the region will take some time to achieve.

4. Conclusions

For this study, CO₂ emissions from energy consumption were calculated using data from eight energy types and five sectors from the Yangtze River Delta region, which included two provinces and one city, from 2000 to 2019. Annual changes and cumulative changes in CO₂ emissions are quantitatively calculated, as well as the contributions of those changes to CO₂ emissions. The Grey prediction GM (1, 1) model was applied to forecast primary energy consumption and CO₂ emissions during 2020–2026. The main findings in this study are as follows.

(1)

Primary energy consumption and CO₂ emissions will continue to rise in the Yangtze River Delta region from 2020 to 2026, with total CO₂ emissions rising by 192.715 million tons over the forecast period;

(2)

Economic output and population size have mainly positive effects on the increase in CO₂ emissions, and the impacts of changes in these two factors led to growth in CO₂ emissions. Economic output is the biggest force pulling up CO₂ emissions, contributing 224.90% in the study period. Population size is the second-most important factor promoting the growth in CO₂ emissions, the cumulative contribution ratio of which is 18.61%;

(3)

Except for 2004 and 2005, energy intensity is the greatest inhibitory factor in reducing CO₂ emissions, with a significant negative effect. The energy intensity effect contributed −140.27% to the change in CO₂ emissions;

(4)

Energy structure and industrial structure have insignificant contributions to CO₂ emissions, contributing −3.75% and 0.51%, respectively. Although energy structure had a positive and negative effect during the study period, it showed a negative effect in terms of the cumulative contribution. Industrial structure had a positive effect on CO₂ emissions except in 2007, although the pull effect was not significant;

(5)

Changes in energy structure and energy intensity had a restraining effect, but they were insufficient to counteract the rise in CO₂ emissions, which led to an overall trend of rising CO₂ emissions.

Global warming has become a complex problem for human civilization. It has also resulted in great challenges to the sustainable development of the Yangtze River Delta region. An important measure to effectively deal with climate change is to reduce greenhouse gas emissions, especially CO₂ emissions due to energy consumption. Based on the results of the above analysis, the following policy recommendations are made.

(1)

Formulating appropriate economic development goals.

According to the results, CO₂ emissions from energy consumption in the Yangtze River Delta region exhibited a general upward trend in 2000–2019, as economic development and energy consumption would tend to commonly inhibit CO₂ emissions decreasing. Local governments must consequently set acceptable economic development goals rather than focusing exclusively on economic growth.

(2)

Optimizing the energy consumption structure.

Despite the recent trend toward declining coal usage, the Yangtze River Delta region still relies heavily on coal for its energy supply. The Yangtze River Delta region should continually optimize the energy structure to reduce environmental pollution and ecological risks. On the one hand, it is efficient to promote the development of clean coal technology and improve coal utilization efficiency. On the other hand, the proportion of green energy consumption needs to be promoted as well as the proportions of wind energy, solar energy, hydropower, and other clean and renewable energy consumption.

(3)

Promoting technological progress and innovation.

Technological progress and innovation are the main driving factors for improving energy efficiency. From the mining of fossil energy to the final use, many processes are involved, resulting in the loss and waste of fossil energy. On the one hand, the government should increase investment in energy technology research and research institutions to carry out technological development and innovation, and on the other, through international cooperation, advanced energy technologies need to be introduced to change the energy use patterns.

(4)

Adjusting industrial structure and developing low-carbon industries.

Rapid economic growth in the Yangtze River Delta region is primarily attributable to the expansion of secondary industry, the region’s main industry sector. This sector is also the largest contributor to energy consumption and CO₂ emissions. The government therefore needs to comprehensively consider the economic and environmental conditions, put forward a development plan to restrain high-polluting enterprises, and formulate relevant regulations for those that are polluting and harmful to the environment. At the same time, local governments should encourage and support enterprises that use clean energy and produce low-carbon products.

(5)

Optimizing the population structure and promoting low-carbon living.

The Yangtze River Delta region has one of the greatest population growth rates in China, and the problems of urbanization and aging are becoming increasingly prominent. On the one hand, the population structure needs to be optimized, and on the other, the government needs to encourage citizens to adopt a low-carbon mindset and help shape their consumption habits. Although some behaviors may not directly lead to resource and energy savings, they can help to create a positive social environment, and raise overall understanding and awareness of energy conservation, towards ultimately living low-carbon lifestyles.