数字经济正在成为经济社会发展的关键因素,减污降碳协同增效是经济社会发展绿色转型的总抓手。文章基于2013—2020年CO2与四类典型大气污染物的排放强度数据,采用时空动态熵值法测度同期中国337个地级及以上市域数字经济发展（DED）水平,提出并运用贝叶斯时空因果森林模型,研究本地和空间邻接周边市域DED（周边WDED）对本地市域减污降碳协同效应（CPCR）的促进效应及其空间分异。结果表明：①中国市域DED和CPCR均呈增长趋势和“西低东高”的空间分布格局。②本地DED和周边WDED对本地CPCR的促进效应分别呈“对数函数”型和“S函数”型特征。③本地DED促进效应的空间分布符合以“胡焕庸线”为界的“西高东低”空间结构,周边WDED促进效应在西部区域和沿海地区较低,而高值区域主要分布在西南和北方地区。为促进减污降碳协同,需要在多个层面加强对数字经济的支持和优化,包括强化区域平衡、关注空间溢出效应、增强区域发达城市的辐射带动作用等。
Digital economy is becoming a key factor in the development of economic society,while pollution reduction and carbon reduction are important constraints for high-quality development. Based on the emission intensity data of CO2 and four types of typical atmospheric pollutants, this study measures the promoting effect of coordination of pollution and carbon reduction (CPCR) at the municipal level in China from 2013 to 2020, and uses the spatiotemporal dynamic entropy method to measure the level of digital economic development (DED) of at the municipal level. It analyzes the promoting effect and its spatial heterogeneity of local DED and spatially adjacent peripheral DED (peripheral WDED) on local CPCR by the means of Bayesian spatiotemporal causal forest model. The conclusions show that: 1) Both DED and CPCR show a growth trend,and their spatial patterns are both "lower in the west of China and higher in the east of China". 2) The promoting effects of local DED and peripheral WDED on local CPCR show the characteristics of "logarithmic function" and "S function", respectively. 3) The spatial distribution of the promoting effect of local DED conforms to the "Hu Huanyong Line" spatial structure,while the promoting effect of peripheral WDED show the distribution characteristics of 'lower in the western and coastal regions, and higher in the southwest and northern regions. To promote CPCR, it is necessary to strengthen support and optimization of the digital economy at multiple levels,including strengthening regional balance, paying attention to spatial spillover effects,and enhancing the regional radiation and driving effects of developed cities.
[1] 易兰,赵万里,杨历. 大气污染与气候变化协同治理机制创新[J]. 科研管理,2020,41(10):134-144.
[2] 周丽,夏玉辉,陈文颖. 中国低碳发展目标及协同效益研究综述[J]. 中国人口·资源与环境,2020,30(7):10-17.
[3] 赵涛,张智,梁上坤. 数字经济,创业活跃度与高质量发展[J]. 管理世界,2020,36(10):65-76.
[4] Li Y,Cui Y,Cai B,et al.Spatial characteristics of CO2 emissions and PM2.5 concentrations in China based on gridded data[J]. Applied Energy,2020,https://doi.org/10.1016/j.apenergy.2020.114852.
[5] Zhang Q Y,Cai B F,Wang M D,et al.City level CO2 and local air pollutants co-control performance evaluation:A case study of 113 key environmental protection cities in China[J]. Advances in Climate Change Research,2022,13(1):118-30.
[6] 原伟鹏,孙慧,王晶,等. 中国城市减污降碳协同的时空演化及驱动力探析[J]. 经济地理,2022,42(10):72-82.
[7] 邓荣荣,张翱祥. 中国城市数字经济发展对环境污染的影响及机理研究[J]. 南方经济,2022,41(2):18-37.
[8] 缪陆军,陈静,范天正,等. 数字经济发展对碳排放的影响——基于 278 个地级市的面板数据分析[J]. 南方金融,2022(2):45-57.
[9] 程云洁,段鑫. 数字经济能促进城市减霾降碳吗?——基于八大国家级大数据试验区的准自然实证分析[J/OL]. 软科学,1-12[2024-01-05]http://kns.cnki.net/kcms/detail/51.1268.G3.20230313.1050.004.html.
[10] Tobler W R.A computer movie simulating urban growth in the Detroit region[J]. Economic Geography,1970,46(s1):234-240.
[11] Zhu J,Chen N,Xing E P.Bayesian inference with posterior regularization and applications to infinite latent svms[J]. The Journal of Machine Learning Research,2014,15(1):1799-1847.
[12] PartridgeA M D,Boarnet M,Brakman S,et al. Introduction:whither spatial econometrics?[J]. Journal of Regional Science,2012,52(2):167-171.
[13] Gibbons S,Overman H G.Mostly pointless spatial econometrics?[J]. Journal of Regional Science,2012,52(2):172-191.
[14] 荆文君,孙宝文. 数字经济促进经济高质量发展:一个理论分析框架[J]. 经济学家,2019,2(2):66-73.
[15] 李治国,王杰. 经济集聚背景下数字经济发展如何影响空间碳排放?[J]. 西安交通大学学报:社会科学版,2022,42(5):87-97.
[16] Du M,Hou Y,Zhou Q,et al.Going green in China:How does digital finance affect environmental pollution?Mechanism discussion and empirical test[J]. Environmental Science and Pollution Research,2022,29(60):89996-90010.
[17] 韦东明,顾乃华,韩永辉. 人工智能推动了产业结构转型升级吗——基于中国工业机器人数据的实证检验[J]. 财经科学,2021(10):70-83.
[18] Zhao N,Liu Y,Cao G,et al.Forecasting China’s GDP at the pixel level using nighttime lights time series and population images[J]. GIScience & Remote Sensing,2017,54(3):407-425.
[19] Li M,Liu H,Geng G,et al.Anthropogenic emission inventories in China:A review[J]. National Science Review,2017,4(6):834-866.
[20] Zheng B,Tong D,Li M,et al.Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions[J]. Atmospheric Chemistry and Physics,2018,18(19):14095-14111.
[21] 张雪玲,焦月霞. 中国数字经济发展指数及其应用初探[J]. 浙江社会科学,2017(4):32-40,157.
[22] 王军,朱杰,罗茜. 中国数字经济发展水平及演变测度[J]. 数量经济技术经济研究,2021,38(7):26-42.
[23] 史金易,王志凯. 加强数字经济认知,推动经济社会迭代创新[J]. 浙江大学学报:人文社会科学版,2021,51(5):149-156.
[24] 李俊明,周佳卉,李杰,等. 基于贝叶斯统计的黄河流域生态文明与共同富裕的时空协同演进研究[J]. 地球信息科学学报,2023,25(4):766-782.
[25] 陶锋,赵锦瑜,周浩. 环境规制实现了绿色技术创新的 “增量提质”吗———来自环保目标责任制的证据[J]. 中国工业经济,2021(2):136-154.
[26] 干春晖,郑若谷,余典范. 中国产业结构变迁对经济增长和波动的影响[J]. 经济研究,2011,46(5):4-16,31.
[27] Brueckner J K.Strategic interaction among governments:An overview of empirical studies[J]. International Regional Science Review,2003,26(2):175-188.
[28] Hahn P R,Murray J S,Carvalho C M.Bayesian regression tree models for causal inference:Regularization,confounding,and heterogeneous effects (with discussion)[J]. Bayesian Analysis,2020,15(3):965-1056.
[29] Krantsevich N,He J,Hahn P R.Stochastic tree ensembles for estimating heterogeneous effects[C]//International Conference on Artificial Intelligence and Statistics. PMLR,2023.
[30] Besag J,York J,Mollié A.Bayesian image restoration,with two applications in spatial statistics[J]. Annals of the Institute of Statistical Mathematics,1991,43(1):1-20.
[31] Rosenbaum P R,Rubin D B.The central role of the propensity score in observational studies for causal effects[J]. Biometrika,1983,70(1):41-55.
[32] Hill J,Linero A,Murray J.Bayesian additive regression trees:A review and look forward[J]. Annual Review of Statistics and Its Application,2020,7(3):251-278.
[33] Sen P K.Estimates of the regression coefficient based on Kendall's tau[J]. Journal of the American Statistical Association,1968,63(324):1379-1389.
[34] Wang J F,Zhang T L,FuU B J. A measure of spatial stratified heterogeneity[J]. Ecological Indicators,2016,67(8):250-256.
[35] J.保罗・埃尔霍斯特. 空间计量经济学:从横截面数据到空间面板[M]. 北京:中国人民大学出版社,2015.
