Carbon Emission Reduction Potential Dataset Balancing per Capita and Benefit in Each of Provinces of China

Zhou, D.¹  Hua, S. R.^2*

1. Institute of Studies for the Great Bay Area, Guangdong University of Foreign Studies, Guangzhou 510006, China;

2. School of Economics and Trade, Guangdong University of Foreign Studies, Guangzhou 510006, China

Abstract: We developed a dataset on the potentialities of carbon emission reduction based on per capita carbon emissions and carbon emissions efficiency metadata extracted for 29 provinces in China. The data were obtained from the China Statistical Yearbook, the China Energy Statistical Yearbook, and China Stock Market & Accounting Research Databases for the period 1997?C2015. Hainan, Tibet, Hong Kong, Macao, and Taiwan were not included in the database because of incomplete data. We converted the fixed capital generated over the study period into uniform values with reference to the constant price in 1952 using an implicit investment deflator in each province. Next, referring to the set depreciation rate and the base period capital stock, we applied the perpetual inventory method to estimate annual capital stocks. Actual GDPs, with reference to the 1952 baseline value, were calculated by dividing the nominal GDP values of the provinces for the period 1997?C2015 by the 1952-based GDP deflator. Total carbon emissions for each province were calculated from fossil fuel combustion and cement consumption values along with associated carbon emission coefficients. These values were then divided by the value for the total provincial population recorded at the end of the year to calculate per capita carbon emission values. In our study, carbon dioxide emissions from the growth of each unit of GDP were considered to reflect the carbon emission intensity. Accordingly, we applied the Super SBM model to measure carbon emissions efficiency levels. We measured the equity of regional carbon emissions based on per capita carbon emissions. Finally, we used the Markov model to calculate the club convergence index of carbon emissions efficiency and fairness to assess their importance in relation to China??s carbon reduction potential, with an emphasis on carbon emissions. The dataset contains 10 tables depicting the following categories of annual provincial-level data for the period 1997?C2015: (1) annual capital stocks, (2) annual GDP values with reference to baseline statistics for 1952, (3) annual per capita carbon emissions, (4) annual carbon emission intensity, (5) carbon emission efficiency calculated using the Super SBM model, 6) energy consumption, (7) Markov transfer probability results for per capita carbon emissions and carbon emissions efficiency in China, (8) a club convergence index model of per capita emissions and the efficiency of regional carbon emission reduction for different temporal durations, (9) degrees of curing based on the results of differential testing of regional per capita carbon emissions and carbon emissions efficiency at a regional scale, and (10) estimation results for the carbon emission reduction potentials of provinces in China based on levels of coordination of per capita and efficiency. The datasets were archived within one data file (with a .xlsx extension) that was 134 KB in size. An analysis of the overall dataset has been published in the Journal of Natural Resources (Vol. 34, No.1, 2019).

Keywords: carbon reduction potential; per capita carbon emissions; carbon emissions efficiency; China

1 Introduction

China??s GDP has increased by about 50% since the implementation of the 12th Five-Year Plan. Excessive CO₂ emissions that have accompanied this rapid economic growth have emerged as an increasingly urgent issue. To achieve the UN Sustainable Development Goals and to safeguard the welfare of the population, the Chinese government has announced a carbon emission intensity reduction target of 60%?C65% of CO₂ emissions per unit of GDP compared with 2005 levels. The development of a more accurate method of measuring the carbon emission reduction potential compared under existing constraints, would facilitate the planning of a more harmonious carbon emission path and the formulation of a rational and scientifically based regional emission reduction policy. This study is aimed at contributing theoretical inputs for ??establishing and improving the economic system of a green and low-carbon development cycle,?? thereby enabling the compelling vision of ??clear waters and lush mountains,?? considered as invaluable assets, to be achieved. Current research on the reduction of carbon emissions has mainly focused on three core principles: the fairness of carbon emissions,^[1?C3] the efficiency of carbon emissions,^[4?C8] and a combination of both principles.^[9?C11] Studies conducted from a singular perspective^[1?C8] do not allow for a comprehensive consideration. While some studies have adopted the principles of fairness and efficiency of carbon emissions,^[9?C11] they have ignored significant variations relating to carbon reduction potential. Consequently, the measurements applied have not been sufficiently scientific and rigorous, thereby reducing the value and effectiveness of policy inputs. To address this gap, we used provincial-level data to calculate the club convergence index of carbon emissions fairness and efficiency in 29 provinces in China for the period 1997?C2015. We subsequently compared the importance of applying these two principles in relation to carbon emissions and assessed the extent of their coordination. Subsequently, we constructed a dataset on the potentialities of carbon emission reduction.

2 Metadata of Dataset

Table 1 presents a summary framework of the Dataset on the Potentialities of Carbon Emission Reduction in China based on Provincial Metadata on per Capita Carbon Emissions and Carbon Emissions Efficiency^[12]. This content includes the full as well as abbreviated names of the dataset, the names of the authors, the geographical region and years of coverage included in the dataset, the data composition, data publisher, and data-sharing policy.

3 Methods

Based on the data for 29 provinces from 1997 to 2015, we measured the efficiency of carbon emissions using the Super SBM model, and the equity of regional carbon emissions on the basis of per capita carbon emissions. Finally, we applied the Markov model to calculate the club convergence index of carbon emissions efficiency and fairness and subsequently assessed their importance with regard to China??s carbon reduction potential, with an emphasis on carbon emissions.

Table 1  Metadata summary of the Dataset on the Potentialities of Carbon Emission Reduction in China based on Provincial Metadata on per Capita Carbon Emissions and Carbon Emissions Efficiency

3.1 Data Collection

We developed a dataset on the potentialities of carbon emission reduction in China. The data for 29 provinces were sourced from the China Statistical Yearbook, the China Energy Statistical Yearbook, and China Stock Market & Accounting Research Databases for the period 1997?C2015. Hainan, Tibet, Hong Kong, Macao, and Taiwan were not included in the database because of incomplete data. Drawing on Shan??s^[14] methodology, we converted the fixed capital generated over the study period into uniform values with reference to the constant price in 1952 using an implicit investment deflator in each province. Then we applied the perpetual inventory method to estimate annual capital stocks based on the set depreciation rate and the base period capital stock. Actual GDPs in relation to the base year (1952) were obtained by dividing the nominal provincial GDPs for the period 1997?C2015 by the 1952-based GDP deflator. Total carbon emissions in each province were calculated from fossil fuel combustion and cement consumption values along with the associated carbon emission coefficient, which were then divided by the value for the total population recorded at the end of the year to calculate per capita carbon emissions. In our study, carbon dioxide emissions from the growth of each unit of GDP reflected the intensity of carbon emissions.

3.2 Algorithm

We expanded the emission reduction potential index developed by Wei et al. ^[13] and calculated carbon emission reduction potential based on balancing per capita carbon emissions and carbon emissions efficiency. Calculation was performed using the following equation:

                                                                                   (1)

where AAC_i is the index for the carbon emission reduction potential of province i; CCL_e and CCL_f respectively denote the weight of carbon emission efficiency and fairness, measured on the basis of solidification??s degree, as specified in club convergence index; E_i and F_i respectively denote standardized carbon emission efficiency and fairness values for province i.

3.3 Research Steps

3.3.1 Application of the Super SBM Model, Incorporating Undesirable Outputs to Measure Carbon Emission Efficiency

Referring to the research of Tone^[15], we applied the Super SBM model to measure the efficiency of carbon reduction. Using data compiled for the 29 provinces for the period 1997?C2015, we included the following indicators of inputs with reference to 1952 (the base year): capital investments measured by capital stocks, labor inputs measured by the total number of employees at the end of year, and energy inputs measured by the total energy consumption. We included GDP data as an indicator of desirable outputs with reference to statistics for 1952. Finally, we considered total carbon emissions as the undesirable output.

3.3.2 Measuring Coordination Levels between Fairness and Efficiency of Carbon Emissions and the Degree of Solidification through the Construction of a Markov Chain Model and a Club Convergence Index

(1) The Markov Model. We applied the concepts and methods proposed by Zhou et al. ^[16] to discretize fairness (efficiency) of carbon emissions within four categories: low, low-medium, high-medium, and high. Next, we calculated the transition probability of fairness (efficiency) between categories. In traditional distributed dynamic models, Markov Chain model usually only entails a consideration of cases in which the duration of a step is 1 a.^[17] We constructed a transition probability matrix covering a period of several years to examine the process of transition of regional carbon emissions fairness (efficiency) over time and to develop a comprehensive knowledge base. The following method was applied to construct the matrix.

The probability value of Markov's transition probability matrix for d years was expressed as Pt,t+d ij = {X_t+d = j??X_t = i}. Pt,t+d ij = {X_t+d = j??X_t = i} indicates that after d years, provinces categorized as type i will transition to type j in the t-th year. The following equation was used to estimate the transition probability:

                                                                                             (2)

where t_n denotes the last period of the investigation, nt,t+d ij denotes the sum of the number of regions that belonged to type i in year t and subsequently transitioned to type j in year t+d over the period of the investigation, and nt i denotes the total number of regions where carbon emission fairness (efficiency) is categorized as type i in the t-th year. We constructed the Markov transition probability matrix of d-year duration based on an estimation of different types of transition probabilities as expressed in equation (3) below:

where the scale of the horizontal area of type i is nd i, which is ??t_n?Cd t=t₀nt i in formula (2). In this equation, pd ii denotes the transition probability of i-type regions remaining as i-types after d

                                                (3)

years. A larger pd ii value indicates a higher degree of solidification of regional discrepancies in carbon fairness (efficiency) while also indicating the existence of club convergence. To compare the degree of solidification of different indexes, we performed a chi-square test.^[18]

(2) A club convergence index considering scale effect. To measure the solidification degree of carbon emissions fairness and efficiency accurately, we constructed a club convergence index on the basis of equation (3). This index accounted for both the sizes of different types of regions (clubs) and the degree of convergence within each club, thereby obtaining the overall degree of club convergence. The following equation was used for the calculation:

                                                   (4)

where pd kk denotes the diagonal element in equation (3), which indicates the degree of convergence of k-type clubs for a duration of d years while nd k/??nd i denotes the proportion of the size of the k-type club.

4 Results and Validation

4.1 Data Composition

The dataset comprised 10 tables that covered the following categories of data compiled for 29 provinces in China for the period 1997?C2015. The details are shown in table 1.

4.2 Data Products

4.2.1 Analysis of the Coordination of Carbon Emissions Fairness and Efficiency: A Dynamic Perspective

We used a Markov Chain model with variable durations to examine the transfer of regional carbon emissions fairness and efficiency between different types. Table 2 shows the transition probabilities for durations of one, three, and five years.

In general, the positions of various types of regions were relatively fixed within the overall distribution. The phenomenon was especially apparent in high-level and low-level regions. Horizontal comparison showed that low and high levels of carbon emission efficiency were associated with higher degrees of solidification. To better compare degrees of solidification of carbon fairness and efficiency, it was necessary to calculate the overall solidification degrees. We integrated solidification data for different level types of regions at a regional scale and calculated club convergence index for durations of one to five years using formula (4). Table 3 shows that club convergence index of carbon emissions efficiency at any time exceeded the solidification degree of carbon emissions fairness. We tested the significance of differences in transfer probabilities to ensure the robustness of the results and calculated the results in each case. Table 4 shows the transfer probability results for durations of one, three, and five years.

Table 2  The Markov transfer probability result of carbon emissions fairness and efficiency in China

Note: L, ML, MH, and H are the four levels of low, medium low, medium high, and high; n is number of samples.

Table 3  Club convergence index model of fairness and efficiency of regional carbon emission reduction under different durations

Table 4  Solidification degree difference test of fairness and efficiency of regional carbon emission

As shown in Table 4, there were significant differences because the test results under different durations all invalidated the null hypothesis and these differences continued to increase over time; this finding was consistent with the results shown in Table 2.

4.2.2 Evaluation of the Carbon Emission Reduction Potentials of Provinces in China According to Levels of Coordination of Fairness and Efficiency

We constructed a club convergence index to evaluate the importance of fairness and efficiency principles in investigation of regional carbon emission reduction potential in China. The average values of club convergence index for carbon emission fairness and efficiency over durations of one to five years were 0.7687 and 0.81672 (in proportions of 0.4849 and 0.5151), respectively. Substituting proportions as weights into formula (1), the results are shown in Table 5.

The results presented in Table 5 indicate that the carbon emission reduction potential of most provinces has improved, as indicated by the level of coordination of carbon emissions fairness and efficiency. These results reveal that, in previous studies, the use of equal weights led to underestimation of the carbon emission reduction potentials of most provinces. Jiangxi, Sichuan, and Guizhou Provinces evidenced relatively large differences in 1997, as did Jiangxi, Henan, Guangdong, Guangxi, Sichuan, and Gansu Provinces in 2015. It reveals that weight adjustment for levels of coordination of fairness and efficiency had a greater impact in these provinces. The carbon emission reduction potential of provinces evidencing increased potential (positive differences) was mainly driven by carbon emissions efficiency. Most of the differences shown in Table 5 are positive, confirming that the solidification problem relating to the efficiency of carbon emissions in China is of more concern.

Table 5  Estimation of carbon emission reduction potential in provinces of China based on the perspective of fairness and efficiency coordination

5 Discussion and Conclusion

We measured the efficiency of carbon reduction in 29 Chinese provinces for the period 1997?C2015. To achieve this, we applied the Super SBM model, incorporating undesirable outputs, and measured the fairness of regional per capita carbon emissions. We subsequently applied the Markov Chain model to calculate the carbon club convergence index values of efficiency and fairness, with the aim of assessing their importance for China??s carbon reduction potential, with an emphasis on carbon emissions. We recalculated the carbon emission potential of each province based on levels of coordination of the principles of fairness and efficiency, thereby providing scientifically based inputs for the government??s formulation of measures to reduce carbon emissions. The results indicated that the degree of carbon club convergence relating to the efficiency of China??s regional carbon emissions was higher and that the curing problem of long-term low efficiency of carbon emissions is of more concern than the ??long-term problem of inequity. A second important finding relates to the underestimation of China??s potential carbon emissions, which will impact the allocation of carbon rights and the sharing of responsibility for reducing emissions within the country.

Author Contributions

Zhou, D. designed the algorithm and is responsible for the overall design of the dataset. Hua, S. R. collected data and wrote the paper.

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