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

Citation:

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

Citation:

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)

References

[1]	张剑, 刘景洋, 董莉,等. 中国能源消费CO₂排放的影响因素及情景分析[J]. 环境工程技术学报, 2023, 13 (1): 71-78.
[2]	中华人民共和国气候变化第四次国家信息通报[EB/OL]. https://www.gov.cn/lianbo/bumen/202312/P020231230296808058475.pdf.
[3]	张慧敏, 魏强, 佟连军. 吉林省产业发展与能源消费实证研究[J]. 地理学报, 2013, 68 (12): 1678-1688.
[4]	张辽, 黄蕾琼. 中国工业企业绿色技术创新效率的测度及其时空分异特征:基于改进的三阶段SBM-DEA模型分析[J]. 统计与信息论坛, 2020, 35(12): 50-61.
[5]	向书江, 杨春梅, 谢雨琦,等. 近20年重庆市主城区碳排放的时空动态演进及其重心迁移[J]. 环境科学, 2023, 44 (1): 560-571.
[6]	王勇, 赵晗. 中国碳交易市场启动对地区碳排放效率的影响[J]. 中国人口·资源与环境, 2019, 29 (1): 50-58.
[7]	AIGNER D, LOVELL C A K, SCHMIDT P. Formulation and estimation of stochastic frontier production function models[J]. Journal of Econometrics, 1977, 6 (1): 21-37.
[8]	FARRELL M J. The measurement of productive efficiency[J]. Journal of the Royal Statistical Society: Series A (General), 1957, 120 (3): 253-281.
[9]	朱顺应, 廖凌云, 吴景安,等. 城市客运交通工具碳排放效率差异:以襄阳市为例[J]. 交通运输系统工程与信息, 2022, 22 (4): 158-166.
[10]	孙秀梅, 王格, 董会忠,等. 基于DEA与SE-SBM模型的资源型城市碳排放效率及影响因素研究:以全国106个资源型地级市为例[J]. 科技管理研究, 2016, 36 (23): 78-84.
[11]	张少华, 蒋伟杰. 能源效率测度方法:演变、争议与未来[J]. 数量经济技术经济研究, 2016, 33 (7): 3-24.
[12]	CHARNES A, COOPER W W, LEWIN A Y, et al. Measuring the efficiency of decision making units[J]. European Journal of Operational Research, 1978, 2 (6): 429-444.
[13]	COOPER W W, PASTOR J T. Generalized efficiency measures (GEMS) and model relations for use in DEA[C].Georgia Productivity Workshop. 1996, 2.
[14]	TONE K. A slacks-based measure of efficiency in data envelopment analysis[J]. European Journal of Operational Research, 2001, 130 (3): 498-509.
[15]	GUO Y, LIU W, TIAN J P, et al. Eco-efficiency assessment of coal-fired combined heat and power plants in Chinese ecoindustrial parks[J]. Journal of Cleaner Production, 2017, 168: 963-972.
[16]	GUO X F, ZHU Q Y, LV L, et al. Efficiency evaluation of regional energy saving and emission reduction in China: a modified slacks-based measure approach[J]. Journal of Cleaner Production, 2017, 140: 1313-1321.
[17]	SHANG Y, LIU H B, LV Y. Total factor energy efficiency in regions of China: an empirical analysis on SBM-DEA model with undesired generation[J]. Journal of King Saud University-Science, 2020, 32 (3): 1925-1931.
[18]	于向宇, 陈会英, 李跃. 基于合成控制法的碳交易机制对碳绩效的影响[J]. 中国人口·资源与环境, 2021, 31 (4): 51-61.
[19]	李欢, 杨珊, 陈建宏,等. 湖南省能源消费碳排放驱动因素及趋势预测实证分析[J]. 环境工程, 2018, 36 (2): 152-157.
[20]	CHEN X, LIN B. Energy and CO₂ emission performance: a regional comparison of China’s non-ferrous metals industry[J]. Journal of Cleaner Production, 2020, 274 (20): 1-15.
[21]	丁绪辉, 张紫璇, 吴凤平. 双控行动下环境规制对区域碳排放绩效的门槛效应研究[J]. 华东经济管理, 2019, 33 (7): 44-51.
[22]	胡剑波, 王楷文. 中国省域碳排放效率时空差异及空间收敛性研究[J]. 管理学刊, 2022, 35 (4): 36-52.
[23]	杜翼, 马静静, 项康利. 基于三阶段DEA模型的福建省能源碳排放效率与影响因素研究[J]. 创新科技, 2019, 19 (10): 8-19.
[24]	杨迪, 杨旭, 吴相利,等. 东北地区能源消费碳排放时空演变特征及其驱动机制[J]. 环境科学学报, 2018, 38 (11): 4554-4565.
[25]	FOTHERINGHAM A S, CRESPO R, YAO J. Exploring, modelling and predicting spatiotemporal variations in house prices[J]. The Annals of Regional Science, 2015, 54: 417-436.
[26]	HU X S, XU H Q. Spatial variability of urban climate in response to quantitative trait of land cover based on GWR model[J]. Environmental monitoring and assessment, 2019, 191: 1-12.
[27]	王旭, 林征, 张志,等. 基于GWR模型的北极滨海平原融冻湖表面温度空间分布模拟[J]. 武汉大学学报(信息科学版), 2016, 41 (7): 918-924.
[28]	BTUNSDON C, FOTHERINGHAM A S, CHARLTON M E. Geographically weighted regression: a method for exploring spatial nonstationarity[J]. Geographical Analysis, 1996, 28 (4): 281-298.
[29]	王正, 樊杰. 能源消费碳排放的影响因素特征及研究展望[J]. 地理研究, 2022, 41 (10): 2587-2599.
[30]	QUE X, MA X G, MA C, et al. A spatiotemporal weighted regression model (STWR V1.0) for analyzing local nonstationarity in space and time[J]. Geoscientific Model Development, 2020,13 (12): 6149-6164.
[31]	QUE X, MA C, MA X G, et al. Parallel computing for fast spatiotemporal weighted regression[J]. Computers & Geosciences, 2021, 150: 104723.
[32]	FAN C, QUE X, WANG Z, et al. Land cover impacts on surface temperatures: evaluation and application of a novel spatiotemporal weighted regression approach[J]. ISPRS International Journal of Geo-Information, 2023, 12 (4): 151.
[33]	QUE X, ZHUANG X H, MA X G, et al. Modeling the spatiotemporal heterogeneity and changes of slope stability in rainfall-induced landslide areas[J]. Earth Science Informatics, 2024, 17(1):51-61.
[34]	ZHANG C Q, CHEN P Y. Applying the three-stage SBM-DEA model to evaluate energy efficiency and impact factors in RCEP countries[J]. Energy, 2022, 241: 122917.
[35]	郭炳南, 曹国勇. 中国各省份碳排放效率与减排潜力测度研究:基于 Undesirable-SBM 超效率模型的实证分析[J]. 生态经济, 2017,33(8): 20-24 , 47.
[36]	袁长伟, 乔丹, 杨颖芳,等. 中国省域交通碳排放强度空间分异与聚类分析[J]. 环境工程, 2018, 36 (7): 185-190.
[37]	刘志华, 徐军委, 张彩虹. 科技创新、产业结构升级与碳排放效率:基于省际面板数据的PVAR分析[J]. 自然资源学报, 2022, 37 (2): 508-520.
[38]	马晓君, 陈瑞敏,董碧滢,等. 中国工业碳排放的因素分解与脱钩效应[J]. 中国环境科学, 2019, 39 (8): 3549-3557.
[39]	刘斌, 潘彤. 地方政府创新驱动与中国南北经济差距:基于企业生产率视角的考察[J]. 财经研究, 2022, 48 (2): 18-32.
[40]	季凯文, 罗璐薏, 齐江波. 新基建赋能高新技术产业的异质性影响研究:基于空间面板计量模型的实证检验[J]. 管理评论, 2023, 35 (2): 28-37.
[41]	CHANGE I P O. 2006 IPCC guidelines for national greenhouse gas inventories[M]. Institute for Global Environmental Strategies, Hayama, Kanagawa, Japan, 2006.
[42]	CHARNES A, COOPER W, LEWIN A Y, et al. Data envelopment analysis theory, methodology and applications[J]. Journal of the Operational Research Society, 1997, 48 (3): 332-333.
[43]	周宁南, 张孝, 刘城山,等. 基于动态时间规整的时序数据相似连接[J]. 计算机学报, 2018, 41(8): 1798-1813.
[44]	PETITJEAN F, KETTERLIN A, GANCARSKI P. A global averaging method for dynamic time warping, with applications to clustering[J]. Pattern Recognition, 2011, 44 (3): 678-693.
[45]	LIN X W, MA J J, CHEN H, et al Carbon emissions estimation and spatiotemporal analysis of China at city level based on multi-dimensional data and machine learning[J]. Remote Sensing, 2022, 14 (13): 3014.
[46]	LV Q, LIU H, WANG J, et al. Multiscale analysis on spatiotemporal dynamics of energy consumption CO₂ emissions in China: utilizing the integrated of DMSP-OLS and NPP-VIIRS nighttime light datasets[J]. Science of the Total Environment, 2020, 703: 134394.
[47]	SONG M, WU J, SONG M R, et al. Spatiotemporal regularity and spillover effects of carbon emission intensity in China’s Bohai Economic Rim[J]. Science of the Total Environment, 2020, 740:140184.
[48]	马雄威. 线性回归方程中多重共线性诊断方法及其实证分析[J]. 华中农业大学学报(社会科学版), 2008 (2): 78-81,85.
[49]	杨涛, 谢紫琦, 胡婧妍,等. 基于回归Shapley值分解法的回采工作面瓦斯涌出量影响因素研究[J]. 煤矿安全, 2023, 54 (11): 18-24.
[50]	NAGELKERKE N J D. A note on a general definition of the coefficient of determination[J]. Biometrika, 1991, 78 (3): 691-692.
[51]	DANIEL G. Brooks (1989) akaike information criterion statistics, technometrics[J]. Technometrics, 1989, 31 (2): 270-271.
[52]	MORGAN J A, TATARR J F. Calculation of the residual sum of squares for all possible regressions[J]. Technometrics, 1972, 14 (2): 317-325.
[53]	方创琳, 李广东, 戚伟,等. "胡焕庸线"东西部城乡发展不平衡趋势及沿博台线微突破策略[J]. 地理学报, 2023, 78 (2): 443-455.
[54]	丁金宏, 程晨, 张伟佳,等. 胡焕庸线的学术思想源流与地理分界意义[J]. 地理学报, 2021, 76 (6): 1317-1333.
[55]	冯冬, 李健. 我国三大城市群城镇化水平对碳排放的影响[J]. 长江流域资源与环境, 2018, 27 (10): 2194-2200.
[56]	LIU Z Y, HAN L, LIU M. Spatiotemporal characteristics of carbon emissions in Shaanxi, China, during 2012—2019: a machine learning method with multiple variables[J]. Environmental Science and Pollution Research, 2023, 30 (37): 87535-87548.
[57]	TANG J X, GONG R Z, WANG H L, et al. Scenario analysis of transportation carbon emissions in China based on machine learning and deep neural network models[J]. Environmental Research Letters, 2023, 18 (6): 064018.
[58]	苏少强, 阙翔, 严宣辉,等. 基于STWR模型的森林病虫影响因素研究[J]. 西北农林科技大学学报(自然科学版), 2022, 50 (11): 81-92.
[59]	BOYD G A, MCCLELLAND J D. The impact of environmental constraints on productivity improvement in integrated paper plants[J]. Journal of Environmental Economics and Management, 1999, 38 (2):121-142.