工业碳排放的影响因素分解及预测：以 深圳市为例

努力减少碳排放、大力发展低碳经济已经成为我国中长期重大发展战略。通过组织开展低碳省区和低碳城市试点建设工作，在深圳等部分试点省市率先试行和实验低碳经济，积极探索符合我国工业化和城市化实际的温室气体减排措施和低碳经济发展道路，本研究基于上述背景开展工业碳排放核算与影响因素分解分析。本研究首先采用排放因子法对深圳市工业二氧化碳排放量进行核算，并追踪碳排放来源，明确深圳市工业碳排放事实依据；其次，采用对数平均迪氏指数法（LMDI）识别影响工业碳排放的主要因素及其对碳排放的贡献；最后，本研究基于回归对人口、富裕和技术的随机影响（STIRPAT）模型综合考虑2009-2019年从业人口、人均产出以及技术进步的碳排放强度数据，并运用岭回归分析方法拟合深圳市工业碳排放模型。同时借助灰色预测模型GM（1,1）模型及情景分析法设定八种不同的情景模式，对2020-2035年深圳市工业碳排放趋势进行预测，并基于研究结果对深圳市碳排放提出政策建议。

研究结果显示：（1）2009-2019年，深圳市工业碳排放整体呈现明显下降趋势。其中深圳市工业碳排放主要来自电力和原煤消耗；从行业角度来看，碳排放源主要为电力及热力的生产和供应业、计算机、通信和其他电子设备制造业；（2）基于LMDI分解法的碳排放影响因素分析显示，能源强度和碳排放系数是抑制深圳市碳排放的主要因素，而从业人口、人均产出和能源结构等因素表现为促进深圳市工业碳排放；（3）通过岭回归拟合得到的STIRPAT模型结果表明，当从业人口、人均产出和包含技术进步的碳排放强度每增加1%时，碳排放则分别增加0.484%、0.650%和0.879%；（4）通过设定八种情景模式分析，在低人口增长率、低人均产出增长率、高碳排放强度下降率的发展情景下，深圳市可以实现既不影响工业经济增长，同时碳排放大幅下降的目标，从而实现社会经济与环境的协调可持续发展

[1] 耿玉超. 全球气候治理中的中国选择[J]. 合作经济与科技, 2022(06): 13-17.
[2] 樊星, 秦圆圆, 高翔. IPCC 第六次评估报告第一工作组报告主要结论解读及建议[J]. 环境保护, 2021, 49(Z2): 44-48.
[3] 钟薇薇, 高海, 徐维军, 等. 多聚类视角下的碳达峰路径探索与趋势研判——基于广东省 21 个地级市面板数据的分析[J]. 南方经济, 2021(12): 58-79.
[4] 张中祥,张钟毓.全球气候治理体系演进及新旧体系的特征差异比较研究[J].国外社会科学,2021,(05):138-150+161.
[5] Global Carbon Project, Global Carbon Budget 2020[R/OL].
[2020-12-11]. https://essd.copernicus.org/articles/12/3269/2020/.
[6] IEA. Global CO2 emissions rebounded to their highest level in history in 2021[R/OL].
[2022-03-08]. https://www.iea.org/news/global - CO2-emissions-rebounded-to-their highest-level-in-history-in-2021.
[7] OUYANG X, LIN B. An analysis of the driving forces of energy-related carbon dioxide emissions in China’s industrial sector[J]. Renewable and sustainable energy reviews, 2015, 45: 838-849.
[8] 深圳特区报, 2021, 单位 GDP 碳排放强度降至全国平均水平的 1/5, 深圳拟推五大举措力争尽早实现碳排放达峰 [EB/OL], 深 圳 : 深圳报业集团 , (2021-03-03)
[2021-05-10]. https://kd.youth.cn/a/30kjmg50dkLDdXx.
[9] 刘学之, 孙鑫, 朱乾坤, 尚玥佟. 中国二氧化碳排放量相关计量方法研究综述[J].生态经济, 2017, 33(11): 21-27.
[10] LIU S, XIAO Q. An empirical analysis on spatial correlation investigation of industrial carbon emissions using SNA-ICE model[J]. Energy, 2021, 224: 120183.
[11] 陈瑞敏. 中国工业碳排放的因素分解与脱钩效应[D]. 东北财经大学, 2019.
[12] HAN X, YU J, XIA Y, et al. Spatiotemporal characteristics of carbon emissions in energy-enriched areas and the evolution of regional types[J]. Energy Reports, 2021, 7: 7224-7237.
[13] CHEN L, XU L, CAI Y, et al. Spatiotemporal patterns of industrial carbon emissions at the city level[J]. Resources, Conservation and Recycling, 2021, 169: 105499.
[14] DONG J, LI C, WANG Q. Decomposition of carbon emission and its decoupling analysis and prediction with economic development: A case study of industrial sectors in Henan Province[J]. Journal of Cleaner Production, 2021, 321: 129019.
[15] ZHAO M, KONG ZH, ESCOBEDO FJ, et al. Impacts of urban forests on off setting carbon emissions from industrial energy use in Hangzhou, China[J]. Journal of Environmental Management, 2010, 91(4): 807-813.
[16] ZHENG Y, DU S, ZHANG X, et al. Estimating carbon emissions in urban functional zones using multi-source data: A case study in Beijing[J]. Building and Environment, 2022, 212: 108804.
[17] LI Q, GAO MF, LI JG. Carbon emissions inventory of farm size pig husbandry combining Manure-DNDC model and IPCC coefficient methodology[J]. Journal of Cleaner Production, 2021, 320: 128854.
[18] 林晓虎, 任婕, 乔俊莲等. 海绵城市建设中碳排放核算研究进展及探析[J]. 资源节约与环保, 2018 (03): 42-44.
[19] NIE S, ZHOU J, YANG F, et al. Analysis of theoretical carbon dioxide emissions from cement production: Methodology and application[J]. Journal of Cleaner Production, 2022, 334: 130270.
[20] 郝千婷, 黄明祥, 包刚. 碳排放核算方法概述与比较研究[J]. 中国环境管理 ,2011, (04): 51-55.
[21] RAN L, YUE R, SHI H, et al. Seasonal and diel variability of CO2 emissions from a semiarid hard-water reservoir[J]. Journal of Hydrology, 2022, 608: 127652.
[22] OHYAMA H, SHIOMI K, KIKUCHI N, et al. Quantifying CO2 emissions from a thermal power plant based on CO2 column measurements by portable Fourier transform spectrometers[J]. Remote Sensing of Environment, 2021, 267: 112714.
[23] ZHU XH, LU KF, PENG ZR, et al. Spatiotemporal variations of carbon dioxide (CO2) at Urban neighborhood scale: Characterization of distribution patterns and contributions of emission sources[J]. Sustainable Cities and Society, 2022, 78:103646.
[24] CHIODINI G, CALIRO S, AVINO R, et al. Hydrothermal pressure-temperature control on CO2 emissions and seismicity at Campi Flegrei (Italy)[J]. Journal of Volcanology and Geothermal Research, 2021, 414: 107245.
[25] WANG J, QUAN Q, CHEN W, et al. Increased CO2 emissions surpass reductions of non-CO2 emissions more under higher experimental warming in an alpine meadow[J]. Science of The Total Environment, 2021, 769: 144559.
[26] ZHANG H, SUN W, LI W, et al. A carbon flow tracing and carbon accounting method for exploring CO2 emissions of the iron and steel industry: An integrated material–energy–carbon hub[J]. Applied Energy, 2022, 309: 118485.
[27] ZHANG P, CAI W, YAO M, et al. Urban carbon emissions associated with electricity consumption in Beijing and the driving factors[J]. Applied Energy, 2020, 275:115425.
[28] LIU X, ZHANG L, HAO Y, et al. Increasing disparities in the embedded carbon emissions of provincial urban households in China[J]. Journal of Environmental Management, 2022, 302: 113974.
[29] YU Y, JIANG T, LI S, et al. Energy-related CO2 emissions and structural emissions’ reduction in China’s agriculture: An input–output perspective[J]. Journal of CleanerProduction, 2020, 276: 124169.
[30] REN FR, TIAN Z, LIU J, et al. Analysis of CO2 emission reduction contribution and efficiency of China’s solar photovoltaic industry: Based on input-output perspective[J]. Energy, 2020, 199: 117493.
[31] XIA Q, TIAN G, WU Z. Examining embodied carbon emission flow relationships among different industrial sectors in China[J]. Sustainable Production and Consumption, 2022, 29: 100-114.
[32] ZHANG G, HUANG Q, SONG K, et al. Responses of greenhouse gas emissions and soil carbon and nitrogen sequestration to field management in the winter season: A 6years measurement in a Chinese double-rice field[J]. Agriculture, Ecosystems & Environment, 2021, 318: 107506.
[33] WANG G, LI Y, LIU J, et al. A two-phase factorial input-output model for analyzing CO2-emission reduction pathway and strategy from multiple perspectives–A case study of Fujian province[J]. Energy, 2022, 248: 123615.
[34] WANG P, LI Y, HUANG G, et al. A multi-scenario factorial analysis and multi regional input-output model for analyzing CO2 emission reduction path in Jing-Jin-Ji region[J]. Journal of Cleaner Production, 2021, 300: 126782.
[35] LIN C, ZHANG L, ZHANG Z. The impact of the rise of emerging economies on global industrial CO2 emissions: Evidence from emerging economies in Regional Comprehensive Economic Partnership[J]. Resources, Conservation and Recycling, 2022, 177: 106007.
[36] PLANT G, KORT EA, MURRAY LT, et al. Evaluating urban methane emissions from space using TROPOMI methane and carbon monoxide observations[J]. Remote Sensing of Environment, 2022, 268: 112756.
[37] HASSANI A, SAFAVI S R, HOSSEINI V. A comparison of light-duty vehicles' high emitters fractions obtained from an emission remote sensing campaign and emission inspection program for policy recommendation[J]. Environmental Pollution, 2021, 286: 117396.
[38] VALERIO AM, Kampel M, Ward ND, et al. CO2 partial pressure and fluxes in the Amazon River plume using in situ and remote sensing data[J]. Continental Shelf Research, 2021, 215: 104348.
[39] DONG Y, JIN G, DENG X. Dynamic interactive effects of urban land-use efficiency, industrial transformation, and carbon emissions[J]. Journal of Cleaner Production, 2020, 270: 122547.
[40] SCHUH AE, OTTE M, LAUVAUX T, et al. Far-field biogenic and anthropogenic emissions as a dominant source of variability in local urban carbon budgets: A global high-resolution model study with implications for satellite remote sensing[J]. Remote Sensing of Environment, 2021, 262: 112473.
[41] WEI W, ZHANG X, ZHOU L, et al. How does spatiotemporal variations and impactfactors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method[J]. Journal of Cleaner Production, 2021, 321: 128933.
[42] LEONTIEF W. The structure of American economy 1919-1939[M], Haravrd UP,Cambridge, Mass, 1941.
[43] JIANG M, AN H, GAO X, et al. Structural decomposition analysis of global carbon emissions: the contributions of domestic and international input changes[J]. Journal of Environmental Management, 2021, 294: 112942.
[44] WANG J, DONG X, DONG K. How digital industries affect China's carbon emissions? Analysis of the direct and indirect structural effects[J]. Technology in Society, 2022, 68: 101911.
[45] TANG Z, YU H, ZOU J. How does production substitution affect China's embodied carbon emissions in exports?[J]. Renewable and Sustainable Energy Reviews, 2022, 156: 111957.
[46] LIU XY, ZHANG LX, HAO Y, et al. Increasing disparities in the embedded carbon emissions of provincial urban households in China[J]. Journal of Environmental Management, 2022, 302: 113974.
[47] XU WH, XIE YL, JI L, et al. Spatial-temporal evolution and driving forces of provincial carbon footprints in China: An integrated EE-MRIO and WA-SDA approach[J], Ecological Engineering, 2022, 176: 106543.
[48] YU M, MENG B, LI R. Analysis of China's urban household indirect carbon emissions drivers under the background of population aging[J]. Structural Change and Economic Dynamics, 2022, 60: 114-125.
[49] XU X, ANG BW. Index decomposition analysis applied to CO2 emission studies[J]. Ecological Economics, 2013, 93: 313-329.
[50] PAULO MD, Effect of generation capacity factors on carbon emission intensity of electricity of Latin America & the Caribbean, a temporal IDA-LMDI analysis[J],Renewable and Sustainable Energy Reviews, 2019, 101: 516-526.
[51] SONG C, ZHAO T, WANG J. Spatial-temporal analysis of China's regional carbon intensity based on ST-IDA from 2000 to 2015[J]. Journal of Cleaner Production, 2019, 238: 117874.
[52] ZHA D, YANG G, WANG Q. Investigating the driving factors of regional CO2emissions in China using the IDA-PDA-MMI method[J]. Energy Economics, 2019, 84: 104521.
[53] WANG Q, HAN X, LI R. Does technical progress curb India's carbon emissions? A novel approach of combining extended index decomposition analysis and production theoretical decomposition analysis[J]. Journal of Environmental Management, 2022, 310: 114720.
[54] CHEN B, XU C, WU Y, et al. Spatiotemporal carbon emissions across the spectrum of Chinese cities: Insights from socioeconomic characteristics and ecological capacity[J]. Journal of Environmental Management, 2022, 306: 114510.
[55] ZHOU X, ZHANG M, ZHOU M, et al. A comparative study on decoupling relationship and influence factors between China's regional economic development and industrial energy–related carbon emissions[J]. Journal of Cleaner Production, 2017, 142: 783-800.
[56] 王凤婷, 方恺, 于畅. 京津冀产业能源碳排放与经济增长脱钩弹性及驱动因素——基于 Tapio 脱钩和 LMDI 模型的实证[J]. 工业技术经济, 2019, 38(8): 32-40.
[57] LIANG W, GAN T, ZHANG W. Dynamic evolution of characteristics and decomposition of factors influencing industrial carbon dioxide emissions in China:1991–2015[J]. Structural Change and Economic Dynamics, 2019, 49: 93-106.
[58] WEN L, LI Z. Provincial-level industrial CO2 emission drivers and emission reduction strategies in China: Combining two-layer LMDI method with spectral clustering[J]. Science of The Total Environment, 2020, 700: 134374.
[59] KöNE A Ç, BüKE T. Factor analysis of projected carbon dioxide emissions according to the IPCC based sustainable emission scenario in Turkey[J]. Renewable energy, 2019, 133 :914-918.
[60] HE Y, XING YT, ZENG XC, et al. Factors influencing carbon emissions from China's electricity industry: Analysis using the combination of LMDI and K-means clustering[J], Environmental Impact Assessment Review, 2022, 93: 106724.
[61] YANG Y, YUAN ZQ, YANG SN, Difference in the drivers of industrial carbon emission costs determines the diverse policies in middle-income regions: A case of northwestern China[J], Renewable and Sustainable Energy Reviews, 2022, 155:111942.
[62] CHEN L, XU L, YANG Z. Accounting carbon emission changes under regional industrial transfer in an urban agglomeration in China's Pearl River Delta[J]. Journal of Cleaner Production, 2017, 167: 110-119.
[63] JIA D, LI CB, WANG QQ, Decomposition of carbon emission and its decoupling analysis and prediction with economic development: A case study of industrial sectors in Henan Province[J], Journal of Cleaner Production, 2021, 321: 129019.
[64] ZHAO M, TAN L, ZHANG W, et al. Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method[J]. Energy, 2010, 35(6): 2505-2510.
[65] NGUYEN DK, HUYNH T LD, NASIR MA. Carbon emissions determinants and forecasting: Evidence from G6 countries[J]. Journal of Environmental Management, 2021, 285: 111988.
[66] WEN HX, CHEN Z, YANG Q, et al. Driving forces and mitigating strategies of CO2emissions in China: A decomposition analysis based on 38 industrial sub-sectors[J]. Energy, 2022, 245: 123262.
[67] WANG Y, NIU Y, LI M, et al. Spatial structure and carbon emission of urban agglomerations: Spatiotemporal characteristics and driving forces[J]. Sustainable Cities and Society, 2022, 78: 103600.
[68] WANG B, YU M, ZHU Y, et al. Unveiling the driving factors of carbon emissions from industrial resource allocation in China: A spatial econometric perspective[J]. Energy Policy, 2021, 158: 112557.
[69] WU L, SUN L, QI P, et al. Energy endowment industrial structure upgrading and CO2emissions in China: Revisiting resource curse in the context of carbon emissions[J]. Resources Policy, 2021, 74: 102329.
[70] KUANG B, LU X, ZHOU M, et al. Provincial cultivated land use efficiency in China: Empirical analysis based on the SBM-DEA model with carbon emissions considered[J]. Technological Forecasting and Social Change, 2020, 151: 119874.
[71] ZANG H, XU Y, ZHAI M, et al. Development of carbon emission interactive network model: A case study of Northeast Industrial District[J]. Journal of Environmental Management, 2021, 300: 113618.
[72] LIN B, MA R. Green technology innovations, urban innovation environment and CO2emission reduction in China: Fresh evidence from a partially linear functional coefficient panel model[J]. Technological Forecasting and Social Change, 2022, 176:121434.
[73] SHU H, XIONG PP. Reallocation planning of urban industrial land for structure optimization and emission reduction: A practical analysis of urban agglomeration in China’s Yangtze River Delta[J]. Land use policy, 2019, 81: 604-623.
[74] SUN L, LI W. Has the opening of high-speed rail reduced urban carbon emissions? Empirical analysis based on panel data of cities in China[J]. Journal of Cleaner Production, 2021, 321: 128958.
[75] DONG H, OHNISHI S, FUJITA T, et al. Achieving carbon emission reduction through industrial & urban symbiosis: A case of Kawasaki[J]. Energy, 2014, 64: 277-286.
[76] SHEN YS, LIN YC, CUI S, et al. Crucial factors of the built environment for mitigating carbon emissions[J]. Science of The Total Environment, 2022, 806:150864.
[77] DOMON S, HIROTA M, KONO T, et al. The long-run effects of congestion tolls, carbon tax, and land use regulations on urban CO2 emissions[J]. Regional Science and Urban Economics, 2022, 92: 103750.
[78] 常慧亮, 佟天宇. 控制 CO2 排放的途径及展望[J]. 炼油与化工, 2022, 33(01): 13-19.
[79] IPCC/UNEP/OECD/IEA. Revised 1996 IPCC guidelines for National Greenhouse Gas Inventories[R]. Paris ： Intergovernmental Panel on Climate Change, United Nations Environment Program, Organization for Economic Co-Operation and Development, International Energy Agency, 1997.
[80] 刘学之, 孙鑫, 朱乾坤等. 中国二氧化碳排放量相关计量方法研究综述[J]. 生态经济, 2017, 33(11): 21-27.
[81] 李研妮. 碳核算的统计范畴、测算方法及指标选择[J]. 金融纵横, 2021, 11: 29-34.
[82] ANG BW. The LMDI approach to decomposition analysis: a practical guide[J]. Energy Policy, 2005, 33(7): 867-871.
[83] ANG BW, ZHANG FQ, CHOI K-H. Factorizing changes in energy and environmental indicators through decomposition[J]. Energy, 1998, 23(6): 489-495.
[84] 张军莉, 刘丽萍. 国内区域碳排放预测模型应用综述[J].环境科学导刊, 2019,38(04): 15-21.
[85] 萨和雅, 王路航, 恩和巴图等.数学模型在区域碳达峰中的应用综述[J]. 内蒙古师范大学学报(自然科学汉文版), 2021, 50(06): 533-538+546+468.
[86] LIU D, XIAO B. Can China achieve its carbon emission peaking? A scenario analysis based on STIRPAT and system dynamics model[J]. Ecological indicators, 2018, 93:647-657.
[87] YANG SG, CAO D, LO K. Analyzing and optimizing the impact of economic restructuring on Shanghai’s carbon emissions using STIRPAT and NSGA-II[J]. Sustainable Cities and Society, 2018, 40: 44-53.
[88] YEH JC, LIAO CH. Impact of population and economic growth on carbon emissions in Taiwan using an analytic tool STIRPAT[J]. Sustainable Environment Research, 2017, 27(1): 41-48.
[89] YANG L, XIA H, ZHANG X, et al. What matters for carbon emissions in regional sectors? A China study of extended STIRPAT model[J]. Journal of Cleaner Production, 2018, 180: 595-602.
[90] HUANG J, LI X, WANG Y, et al. The effect of energy patents on China's carbon emissions: Evidence from the STIRPAT model[J]. Technological Forecasting and Social Change, 2021, 173: 121110.
[91] 赵俸一 . 基于系统动力学模型的河北省碳排放预测研究 [D]. 华北电力大学 ,2019.
[92] WANG H, CAO R, ZENG W. Multi-agent based and system dynamics models integrated simulation of urban commuting relevant carbon dioxide emission reduction policy in China[J]. Journal of Cleaner Production, 2020, 272: 122620.
[93] MAO G, DAI X, WANG Y, et al. Reducing carbon emissions in China: Industrial structural upgrade based on system dynamics[J]. Energy Strategy Reviews, 2013, 2(2): 199-204.
[94] LIU LY, ZHENG BH, BEDRA KB. Quantitative analysis of carbon emissions for new town planning based on the system dynamics approach[J]. Sustainable Cities and Society, 2018, 42: 538-546.
[95] FONG WK, MATSUMOTO H, LUN YF. Application of system dynamics model as decision making tool in urban planning process toward stabilizing carbon dioxide emissions from cities[J]. Building and Environment, 2009, 44(7): 1528-1537.
[96] 曲鲁平, 翟腾腾, 张全景. 基于灰色理论模型的山东省土地利用碳排放研究[J].山东农业大学学报(自然科学版), 2019, 50(02): 290-296.
[97] QIAN W, WANG J. An improved seasonal GM (1,1) model based on the HP filter for forecasting wind power generation in China[J]. Energy, 2020, 209: 118499.
[98] GUO J, LIU W, TU L, et al. Forecasting carbon dioxide emissions in BRICS countries by exponential cumulative grey model[J]. Energy Reports, 2021, 7: 7238-7250.
[99] HUO T, XU L, FENG W, et al. Dynamic scenario simulations of carbon emission peak in China's city-scale urban residential building sector through 2050[J]. Energy Policy, 2021, 159: 112612.
[100] WANG M, WANG P, WU L, et al. Criteria for assessing carbon emissions peaks at provincial level in China[J]. Advances in Climate Change Research, 2022, 13(1): 131-137.
[101] SUN W, HUANG C. Predictions of carbon emission intensity based on factor analysis and an improved extreme learning machine from the perspective of carbon emission efficiency[J]. Journal of Cleaner Production, 2022: 130414.
[102] QIAO W, LU H, ZHOU G, et al. A hybrid algorithm for carbon dioxide emissions forecasting based on improved lion swarm optimizer[J]. Journal of Cleaner Production, 2020, 244: 118612.
[103] YANG M. Models for evaluating the regional carbon emissions performance under the background of urban industrial agglomerations with hesitant fuzzy linguistic information[J]. International Journal of Knowledge-based and Intelligent Engineering Systems, 2017, 21(4): 235-242.
[104] LIU Z, JIANG P, WANG J, et al. Ensemble system for short term carbon dioxide emissions forecasting based on multi-objective tangent search algorithm[J]. Journal of Environmental Management, 2022, 302: 113951.
[105] GAO M, YANG H, XIAO Q, et al. A novel method for carbon emission forecasting based on Gompertz's law and fractional grey model: Evidence from American industrial sector[J]. Renewable energy, 2022, 181: 803-819.
[106] MA X, JIANG P, JIANG Q. Research and application of association rule algorithm and an optimized grey model in carbon emissions forecasting[J]. Technological Forecasting and Social Change, 2020, 158: 120159.
[107] REN F, LONG D. Carbon emission forecasting and scenario analysis in Guangdong Province based on optimized Fast Learning Network[J]. Journal of Cleaner Production, 2021, 317: 128408.
[108] QIAO Z, MENG X, WU L. Forecasting carbon dioxide emissions in APEC member countries by a new cumulative grey model[J]. Ecological indicators, 2021, 125:107593.
[109] ZHOU W, ZENG B, WANG J, et al. Forecasting Chinese carbon emissions using a novel grey rolling prediction model[J]. Chaos, Solitons & Fractals, 2021, 147:110968.
[110] YANG H, O’CONNELL JF. Short-term carbon emissions forecast for aviation industry in Shanghai[J]. Journal of Cleaner Production, 2020, 275: 122734.
[111] York R, Rosa EA, Dietz T. Bridging environmental science with environmental policy: Plasticity of population, affluence, and technology[J]. Social Science Quarterly, 2002, 83(1): 18-34.
[112] WAGGONER PE, AUSUBEL JH. A framework for sustainability science: A renovated IPAT identity[J]. Proceedings of the National Academy of Sciences, 2002, 99(12): 7860-7865.
[113] ROSA EA, DIETZ T. Climate change and society: Speculation, construction and scientific investigation[J]. International sociology, 1998, 13(4): 421-455.
[114] HOERL AE, KENNARD RW. Ridge regression: Biased estimation for nonorthogonal problems[J]. Technometrics, 1970, 12(1): 55-67.
[115] 蒋译漫. 区域视角下的碳排放影响因素比较研究[D]. 重庆大学, 2019.
[116] DENG JL. Control problems of grey systems[J]. Systems & control letters, 1982, 1(5): 288-294.
[117] LIU S. The trap in the prediction of a shock disturbed system and the buffer operator[J]. Journal-Huazhong University of Science and Technology Chinese Edition, 1997, 25: 25-27.
[118] CUI J, LIU SF, ZENG B, et al. A novel grey forecasting model and its optimization[J]. Applied Mathematical Modelling, 2013, 37(6): 4399-4406.
[119] 张之弘. 灰色离散 GM(1,1)优化模型及其应用[D]. 重庆大学, 2017.
[120] FENG R, LONG DH, Carbon emission forecasting and scenario analysis in Guangdong Province based on optimized Fast Learning Network[J], Journal of Cleaner Production, 2021, 317: 128408.
[121] TCFD, Task Force on Climate-related Financial Disclosures [R/OL].
[2021-10-14]. https://assets.bbhub.io/company/sites/60/2020/09/2020-TCFD_Guidance-Scenario Analysis-Guidance.pdf.
[122] TAN XC, DONG LL, CHEN DX, China’s regional CO2 emissions reduction potential: A study of Chongqing city[J], Applied Energy, 2016, 162: 1345-1354.