SPATIOTEMPORAL HETEROGENEITY ANALYSIS OF ENERGY CARBON EMISSION EFFICIENCY IN CHINA BASED ON SBM-DEA AND STWR MODEL

FEI Tingting; DING Xiaoting; QUE Xiang; LIN Jin; LIN Jian; WANG Ziwei; LIU Jinfu

doi:10.13205/j.hjgc.202410022

Volume 42 Issue 10

Oct. 2024

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FEI Tingting, DING Xiaoting, QUE Xiang, LIN Jin, LIN Jian, WANG Ziwei, LIU Jinfu. SPATIOTEMPORAL HETEROGENEITY ANALYSIS OF ENERGY CARBON EMISSION EFFICIENCY IN CHINA BASED ON SBM-DEA AND STWR MODEL[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(10): 188-200. doi: 10.13205/j.hjgc.202410022

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SPATIOTEMPORAL HETEROGENEITY ANALYSIS OF ENERGY CARBON EMISSION EFFICIENCY IN CHINA BASED ON SBM-DEA AND STWR MODEL

doi: 10.13205/j.hjgc.202410022

FEI Tingting¹,
DING Xiaoting^1,2,
QUE Xiang^{1,2,3
,
,},
LIN Jin¹,
LIN Jian^1,2,
WANG Ziwei¹,
LIU Jinfu^1,3

1. College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China;
2. Fujian Statistical Information Research Center, Fuzhou 350002, China;
3. Key Laboratory for Ecology and Resource Statistics of Fujian Province, Fuzhou 350002, China

Received Date: 2023-08-20
Available Online: 2024-11-30

Abstract

Abstract

Analysing the spatiotemporal heterogeneity of China’s energy carbon emission efficiency is one of the keys to researching and formulating regional energy carbon emission efficiency improvement strategies and accelerating the realization of the Double Carbon goal. This study proposed a framework for analyzing spatiotemporal heterogeneity of energy carbon emissions based on the combination of the SBM-DEA (slack-based measure-data envelopment analysis) and the spatiotemporal weighted regression (STWR) model. Firstly, the energy carbon emission efficiency index (ECEI) was calculated based on SBM-DEA model. Then, the spatiotemporal non-stationary relationships between the efficiency index and its main driving forces from 2012 to 2019, i.e., the degree of opening to the outside world, the level of urbanization, the investment in science and technology, and the proportion of coal consumption were built by using STWR. Furthermore, based on the dynamic time warping algorithm (dynamic time warping, DTW), the similarity between the time series of different coefficients corresponding to each independent variable, which was generated by the STWR model, was calculated. The K-Medoids were employed to cluster based on the similarity with using the Elbow method to determine the optimal cluster number of K. The results show that China’s energy carbon emissions are generally increasing, but the ECEI has not improved. 1) Among them, the degree of openness to the ECEI presents a ladder distribution from the western to the eastern, and the overall positive impact intensity is western regions>central regions>eastern regions. 2) Most of the impact of urbanization level is positive, the degree increases first and then decreases, and reaches the maximum in 2015. The urbanization level in southern China has a U-shaped relationship with energy and carbon emission efficiency, and the degree of negative impact first increases and then decreases. 3) The investment in science and technology is mainly positively correlated with the efficiency of energy and carbon emissions. The coastal areas are relatively stable, and the positive impact of Hubei and Hunan is gradually increasing, while the three northeastern provinces (Heilongjiang, Jilin, and Liaoning) and Sichuan are negatively correlated. 4) From 2013 to 2017, the proportion of coal consumption in each region had a negative impact on energy carbon emission efficiency, concentrated in the central region and showed an extension trend, and its influence gradually was weakened. The proportion of coal consumption in Hebei, Henan, and Shaanxi has a large negative impact on energy carbon emission efficiency, showing a W shape. The proposed analysis framework can conduct a multi-dimensional evaluation of environmental impact, resource consumption, and social value, and measure China’s carbon emission efficiency more scientifically and reasonably. It is the first time to introduce the STWR model, which can be used to explore the sub-stationarity relationship between the energy carbon emission efficiency value and the main driving factors and its change over time. Clustering of time-series coefficients can help identify the spatiotemporal pattern of carbon emission efficiency and support decision-making to coordinate regional energy consumption and carbon dioxide emissions dynamically and rationally.
- spatiotemporal weighted regression,
- data envelopment analysis,
- energy and carbon emission efficiency,
- dynamic time warping algorithm

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References(59)

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