COUPLING COORDINATION BETWEEN ENERGY CONSUMPTION CARBON EMISSION EFFICIENCY AND ECOLOGICAL RESILIENCE UNDER “DUAL CARBON”

doi:10.13776/j.cnki.resourcesindustries.20240509.004

Abstract

Abstract: Energy system entangles with ecosystem with mutual promotion and constraints.Advancement of coordination between carbon emission efficiency of energy consumption and ecological resilience is key to China's “dual carbon” strategy and quality economy.To further study their coupling coordination, this paper establishes an index system of carbon emission of energy consumption and ecological resilience based on China's 30 provinces (autonomous regions and municipalities), and applies coupling coordination model, spatial autocorrelation model and GM-ARIMA model to study 2004 to 2021 coupling coordination and its temporal-spatial evolution of China's 30 provinces in their carbon emission of energy consumption and ecological resilience, and forecasts their 2022 to 2026 coupling coordination.Their mean value 2004 to 2021 carbon emission efficiency of energy consumption and ecological resilience are less than 0.6 and 0.7, classified four coupling coordination types, basic coordinated, near mis-coordinated, light mis-coordinate, moderate mis-coordinated.From time scale, their coupling coordination during 2004 to 2021 had undergone fluctuated rising, fluctuated falling, continuously falling and stably unchanging trends.Geographically, their coupling coordination has a strong positive spatial auto-correlation with their local spatial concentrating model to be average.Forecast shows that their 2022 to 2026 coupling coordination values of scenario 2 and 3 is higher than scenario 1, suggesting decreasing carbon emission intensity can not only prevent ecological destruction, but also boost the transformation and upgrade of energy consuming structures.

Key words: carbon emission efficiency of energy consumption, ecological resilience, coupling coordination model, spatiotemporal analysis

摘要： 能源系统与生态系统之间存在着相互促进、相互制约等复杂关系，促进能源消费碳排放效率与生态韧性的协调发展对于我国贯彻新发展理念、实现“双碳”战略目标，以及推动经济社会高质量发展均具有十分重要的积极意义。为了深入开展对能源消费碳排放效率与生态韧性的耦合协调度研究，本文以我国30个省份（自治区、直辖市）为研究对象，通过构建能源消费碳排放效率与生态韧性指标体系，运用耦合协调度模型、空间自相关模型以及GM-ARIMA模型，来测度分析和揭示2004—2021年我国30个省份能源消费碳排放效率与生态韧性的耦合协调度及其耦合协调度的时空演变特征，并且还对2022—2026年我国各省份能源消费碳排放效率与生态韧性的耦合协调度做出了预测分析。研究结果表明：1）从整体上看，2004—2021年我国30个省份的能源消费碳排放效率均值与生态韧性均值都分别小于0.6和0.7，耦合协调度类型主要有勉强协调、濒临失调、轻度失调、中度失调4种。2）从时序上看，2004—2021年我国30个省份的能源消费碳排放效率与生态韧性的耦合协调度分别呈现出了波动上升、波动下降、持续下降、平稳不变4种不同的变化趋势。3）从空间上看，2004—2021年我国30个省份的能源消费碳排放效率与生态韧性的耦合协调度均存在着显著的正向空间自相关性，且各省份的局部空间集聚模式都显现得相对平均。4）从预测结果上看，2022—2026年我国30个省份在情景2和情景3的能源消费碳排放效率与生态韧性的耦合协调度值要明显高于情景1，说明降低碳排放强度不但可以有效防治对生态的破坏，而且还能够推动经济社会能源消费结构的转型升级。

关键词: 能源消费碳排放效率, 生态韧性, 耦合协调度模型, 时空分析

CLC Number:

F206
F124.5

ZHU Zhiming, XU Jie, LI Hongyan, et al.. COUPLING COORDINATION BETWEEN ENERGY CONSUMPTION CARBON EMISSION EFFICIENCY AND ECOLOGICAL RESILIENCE UNDER “DUAL CARBON”[J]. Resources & Industries, 2024, 26(3): 21-35.

朱智洺, 徐婕, 李红艳, 等. “双碳”背景下能源消费碳排放效率与生态韧性耦合协调研究[J]. 资源与产业, 2024, 26(3): 21-35.

References

郭茹, 田博文, 吕爽, 2022. 基于SBM模型的区域碳排放效率评估研究[J]. 能源环境保护, 36（5）: 13-17.〔GUO R, TIAN B W, LV S, 2022. Evaluation of regional carbon emission efficiency based on SBM model[J]. Energy Environment Protection, 36（5）: 13-17.〕

华坚, 朱文静, 黄媛媛, 2021. 制造业与生产性服务业协同集聚对绿色发展效率的影响: 以长三角城市群27个中心城市为例[J]. 资源与产业, 23（2）: 61-72.〔HUA J, ZHU W J, HUANG Y Y, 2021. A case study on 27 central cities of Yangtze river delta city cluster: impacts of coordinated concentration of manufacturing and industrial servicing on green developing efficiency[J]. Resources & Industries, 23（2）: 61-72.〕

金波, 刘文宁, 2022. 本土市场规模与能源效率: 绿色技术进步、绿色技术效率的调节路径[J]. 生态经济, 38（10）: 70-79, 118.〔JIN B, LIU W N, 2022. Home market size and energy efficiency: moderated dual-path of green technology progress and green technology efficiency[J]. Ecological Economy, 38（10）: 70-79, 118.〕

李建豹,黄贤金,揣小伟,等, 2020. 长三角地区碳排放效率时空特征及影响因素分析[J]. 长江流域资源与环境,29（7）: 1486-1496.〔LI J B, HUANG X J, CHUAI X W, et al., 2020. Spatiotemporal characteristics and influencing factors of carbon emission efficiency in Yangtze River Delta region[J]. Resources and Environment in the Yangtze Basin, 29（7）: 1486-1496.〕

李金铠, 马静静, 魏伟, 2020. 中国八大综合经济区能源碳排放效率的区域差异研究[J]. 数量经济技术经济研究, 37（6）: 109-129.〔LI J K, MA J J, WEI W, 2020. Study on regional differences of energy carbon emission efficiency in eight economic areas of China[J]. Journal of Quantitative & Technical Economics, 37（6）: 109-129.〕

林向义, 牛艺, 罗洪云, 等, 2021. 黑龙江省工业企业能源效率测算及区域差异研究[J]. 资源与产业, 23（2）: 39-47.〔LIN X Y, NIU Y, LUO H Y, et al, 2021. Energy efficiency measurement and regional difference of industrial enterprises in Heilongjiang province[J]. Resources & Industries, 23(2): 39-47.〕

刘琼芳, 2021. 基于ARIMA-GM模型的福建省经济人口承载力预测研究[J]. 福建金融管理干部学院学报（2）: 45-53, 60.〔LIU Q F, 2021. Prediction of economic population carrying capacity in Fujian Province based on ARIMA-GM model[J]. Journal of Fujian Institute of Financial Administrators（2）: 45-53, 60.〕

刘源, 2016. 基于GMARIMA模型的成都市生活垃圾产量预测研究[D]. 成都: 西南交通大学.〔LIU Y, 2016. Research of Chengdu domestic waste output forecast based on GM-ARIMA model[D]. Chengdu: Southwest Jiaotong University.〕

陆菊春, 王新怡, 2021. 长江经济带城市绿色创新效率的时空特征及影响因素[J]. 科技管理研究, 41（20）: 224-232.〔LU J C, WANG X Y, 2021. Spatiotemporal characteristics and influencing factors of urban green innovation efficiency in the Yangtze River Economic Belt[J]. Science and Technology Management Research, 41（20）: 224-232.〕

马世骏, 王如松, 1984. 社会-经济-自然复合生态系统[J]. 生态学报（1）: 1-9.〔MA S J, WANG R S, 1984. The social-economic-natural complex ecosystem[J]. Acta Ecologica Sinica（1）: 1-9.〕

宁论辰, 郑雯, 曾良恩, 2021. 2007—2016年中国省域碳排放效率评价及影响因素分析: 基于超效率SBM-Tobit模型的两阶段分析[J]. 北京大学学报(自然科学版), 57（1）: 181-188.〔NING L C, ZHENG W, ZENG L E, 2021. Research on China's carbon dioxide emissions efficiency from 2007 to 2016: based on two stage super efficiency SBM model and Tobit model[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 57（1）: 181-188.〕

彭翀, 林樱子, 吴宇彤, 等, 2021. 基于“成本-能力-能效”的长江经济带城市韧性评估[J]. 长江流域资源与环境, 30（8）: 1795-1808.〔PENG C, LIN Y Z, WU Y T, et al., 2021. Urban resilience evaluation of Yangtze River Economic Belt based on “cost-capacity-efficiency”[J]. Resources and Environment in the Yangtze Basin, 30（8）: 1795-1808.〕

彭俊杰, 2022. 黄河流域“水-能源-粮食”纽带系统的耦合协调及时空分异[J]. 区域经济评论（2）: 51-59.〔PENG J J, 2022. Coupling relationship and spatial-temporal differentiation of the water-energy-food nexus in the Yellow River Basin[J]. Regional Economic Review（2）: 51-59.〕

皮建才, 宋大强, 2021. 中国制造业与房地产业协调发展的测度与判断[J]. 金融研究（9）: 72-90.〔PI J C, SONG D Q, 2021. Measuring and analyzing coordinated development between manufacturing industry and the real estate industry in China[J]. Journal of Financial Research（9）: 72-90.〕

孙久文, 崔雅琪, 张皓, 2022. 黄河流域城市群生态保护与经济发展耦合的时空格局与机制分析[J]. 自然资源学报, 37（7）: 1673-1690.〔SUN J W, CUI Y Q, ZHANG H,2022.Spatio-temporal pattern and mechanism analysis of coupling between ecological protection and economic development of urban agglomerations in the Yellow River Basin[J].Journal of Natural Resources, 37（7）: 1673-1690.〕

田瑞, 2021. 基于DEA模型的河南省粮食生产要素投入效率评价分析[J]. 南方农业, 15（31）: 60-64.〔TIAN R, 2021. Evaluation and analysis on rice production factors in Henan province based on DEA model[J]. South China Agriculture,15（31）: 60-64.〕

万统帅, 李燕, 曾丽婷, 等, 2021. 区域视角下生态韧性测度研究: 以云南省16个地州市为例[J]. 中国资源综合利用, 39（10）: 44-47, 52.〔WAN T S, LI Y, ZENG L T, et al., 2021. Research on the measurement of ecological resilience from the regional perspective: take 16 prefectures and cities in Yunnan Province as example[J]. China resources comprehensive utilization, 39（10）: 44-47, 52.〕

王保乾, 李昕燃, 2022. 基于水足迹与SBM-Malmquist模型的长江经济带工业水资源绿色效率的研究[J]. 资源与产业, 24（3）: 21-31.〔WANG B Q, LI X R, 2022. Green efficiency of industrial water resource in Yangtze River economic zone based on water footprint and SBM-Malmquist model[J]. Resources &Industries, 24（3）: 21-31.〕

王建鹏, 尹士, 马立军, 等, 2022. 基于非期望产出的河北省耕地生态效率时空演变及收敛性分析[J]. 水土保持研究, 29（6）: 81-89.〔WANG J P, YIN S, MA L J, et al., 2022. Spatiotemporal evolution and influencing factors of cultivated land ecological efficiency in Hebei Province based on SBM model[J]. Research of Soil and Water Conservation, 29（6）: 81-89.〕

王凯, 夏莉惠, 陈勤昌, 等, 2018. 基于空间聚类分析的中国旅游业碳排放效率[J]. 环境科学研究, 31（3）: 419-427.〔WANG K, XIA L H, CHEN Q C, et al., 2018. Carbon emission efficiency in China's tourism industry by spatial clustering analysis[J]. Research of Environmental Sciences,31（3）: 419-427.〕

汪顺生, 杨金月, 王爱丽, 等, 2022. 河南省黄河流域水资源-经济-生态系统耦合协调评价及预测[J]. 湖泊科学, 34（3）: 919-934.〔WANG S S, YANG J Y, WANG A L, et al., 2022. Evaluation and forecast of coupling coordination of water resources, economy and ecosystem in the Yellow River Basin of Henan Province[J]. Journal of Lake Sciences, 34（3）: 919-934.〕

王雪微, 杨旭, 赵程, 等, 2019. 黑龙江省资源型城市资源与环境耦合协调时空演化特征研究[J]. 哈尔滨师范大学自然科学学报, 35（1）: 93-96.〔WANG X W, YANG X, ZHAO C, et al., 2019. Study on the spatio-temporal evolution characteristics of the coupling and coordination ofresources and environment in Heilongjiang resource-based cities[J]. Natural Sciences Journal of Harbin Normal University, 35（1）: 93-96.〕

汪永生, 李玉龙, 王文涛, 2022. 中国海洋生态经济系统韧性的时空演化及障碍因素[J]. 生态经济, 38（5）: 53-59.〔WANG Y S, LI Y L, WANG W T, 2022. Spatial and temporal changes and obstacles factors of marine ecological-economic resilience in China[J]. Ecological Economy, 38（5）: 53-59.〕

王兆峰, 杜瑶瑶, 2019. 基于SBM-DEA模型湖南省碳排放效率时空差异及影响因素分析[J]. 地理科学, 39（5）: 797-806.〔WANG Z F, DU Y Y, 2019. Spatial-temporal differences and influencing factors of carbon emission efficiency in Hunan Province based on SBM-DEA model[J]. Scientia Geographica Sinica, 39（5）: 797-806.〕

夏楚瑜, 董照樱子, 陈彬, 2022. 城市生态韧性时空变化及情景模拟研究: 以杭州市为例[J]. 生态学报, 42（1）: 116-126.〔XIA C Y, DONG Z Y Z, CHEN B, 2022. Spatio-temporal analysis and simulation of urban ecological resilience: a case study of Hangzhou[J]. Acta Ecologica Sinica, 42（1）: 116-126.〕

张琳, 张志浩, 宋扬, 2022. 基于GM(1,1)模型的生物质材料产生量预测[J]. 应用能源技术（10）: 23-27.〔ZHANG L, ZHANG Z H, SONG Y, 2022. Biomass material production forecast based on GM(1,1) model[J]. Applied Energy Technology（10）: 23-27.〕

张明斗, 任衍婷, 2022. 环境规制对生态韧性的影响: 基于“本地-邻地”效应的视角[J]. 北京理工大学学报(社会科学版), 24（6）: 16-29.〔ZHANG M D, REN Y T, 2022. Impact of environmental regulation on ecological resilience: a perspective of“local-neighborhood” effect[J]. Journal of Beijing Institute of Technology（Social Sciences Edition）, 24（6）: 16-29.〕

赵婷, 2019. 低碳视角下陕西省能源消费与环境污染双向耦合关系建模分析研究[J]. 环境科学与管理, 44（4）: 27-31.〔ZHAO T, 2019. Modeling analysis of two-way coupling relationship between energy consumption and environmental pollution in Shaanxi Province from low carbon perspective[J]. Environment Science and Management, 44（4）: 27-31.〕

周宏浩, 陈晓红, 2019. 中国资源型城市精明发展与环境质量的耦合关系及响应机制[J]. 自然资源学报, 34（8）: 1620-1632.〔ZHOU H H, CHEN X H, 2019. Coupling relationship and its response mechanism between smart development and environmental quality in Chinese resource-based cities[J]. Journal of Natural Resources, 34（8）: 1620-1632.〕

朱智洺, 桂梦婷, 李红艳, 2023. “双碳”目标下江苏省碳平衡潜力预测研究[J]. 资源与产业, 25（1）: 1-13.〔ZHU Z M, GUI M T, LI H Y, 2023. Forecast of Jiangsu's carbon balance potential under the “dual carbon” objectives[J]. Resources & Industries, 25（1）: 1-13.〕

宗鑫, 杨浩, 2021. 新型城镇化与城市低碳发展时空耦合关系及驱动力因素分析[J]. 生态经济, 37（4）: 80-87.〔ZONG X, YANG H, 2021. Analysis on the spatio temporal coupling relationship and driving forces of new urbanization and urban low carbon development[J]. Ecological Economy, 37（4）: 80-87.〕

BENNETT E M, CUMMING G S, PETERSON G D, 2005. A systems model approach to determining resilience surrogates for case studies[J]. Ecosystems, 8（8）: 945-957.

HOLLING C S, 1973. Resilience and stability of ecological systems[J]. Annual Review of Ecology and Systematics, 4（1）: 1-23.

LI S Y, LI R R, 2017. Comparison of forecasting energy consumption in Shandong, China using the ARIMA model, GM model, and ARIMA-GM model[J]. Sustainability, 9（7）: 1181.

WALKER B H, HOLLNG C S, CARPENTER S R, et al., 2003. Resilience, adaptability and transformability in social-ecological systems[J]. Ecology and Society, 9（2）: 090205.

[1]	TIAN Ze, XIAO Lingying, LIANG Wei, et al. COUPLING COORDINATION BETWEEN INDUSTRIAL GREEN LOW-CARBON TRANSFORMATION AND QUALITY ECONOMIC DEVELOPMENT IN YELLOW RIVER STREAM [J]. Resources & Industries, 2023, 25(1): 14-26.
[2]	FU Chen-ling, ZHANG Xiao-ping, ZHANG Zhi-bin, et al.. COUPLING RELATIONSHIP BETWEEN HUANGSHAN’S TOURISM INDUSTRY AND LOW- CARBON CITY [J]. Resources & Industries, 2017, 19(1): 1-9.