在全球气候治理与环境污染协同应对的时代背景下，碳排放作为主要温室气体排放源，已成为制约可持续发展的关键挑战。陕西省作为我国中西部地区的工业大省和经济强省，其低碳发展路径对区域乃至全国的可持续发展具有重要示范意义。本文聚焦于陕西省工业碳排放的影响因素和情景预测研究，旨在通过系统性分析为其低碳转型提供科学合理的对策建议。

本文在碳排放相关概念和低碳经济理论、可持续发展理论等的基础上，首先对陕西省工业发展现状进行分析，发现陕西省的工业能源消费结构比较单一，能源强度总体呈现波动下降趋势，应用碳排放系数法对2010-2023年工业碳排放量进行测算，呈现逐渐增加的趋势；其次运用灰色关联分析、STIRPAT模型和岭回归法对陕西省工业碳排放量的影响因素进行分析，其中灰色关联分析结果显示，在初始选定的十个指标中，除建成区绿化覆盖率、居民消费价格指数、工业企业单位数外，其余七个指标：人口规模、人均GDP、能源结构、能源强度、城镇化率、工业增加值、产业结构的关联度均超过0.75，均与碳排放量存在较高的关联性，其中能源强度、城镇化率的解释效力在众多影响因子中表现突出。岭回归估计结果表明，人口规模、人均GDP、能源结构、能源强度、城镇化率、工业增加值、产业结构均与碳排放量呈显著正向关联，通过消除多重共线性，建立最终STIRPAT扩展模型；再次，本文运用情景分析法构建了基准情景、低碳情景和高碳情景三种情景，对不同情景下2024-2035年陕西省的工业碳排放量进行了预测，研究结果表明，在低碳情景下，陕西省有望如期实现碳达峰目标；最后对陕西省工业碳减排提出对应的对策建议，主要包括：（1）优化工业能源结构；（2）加快产业结构升级；（3）加快科学技术的进步及创新；（4）强化生态补偿和环境监测；（5）加强政策激励与公众参与意识；（6）建设生态工业园区。

综上所述，本研究一方面对陕西省推动低碳转型与可持续发展具有重要实践意义，通过碳排放情景的科学预测和深度剖析，能够为陕西省制定适配省情的低碳发展目标及政策体系提供关键决策依据，另一方面其方法论框架与实施路径亦能为其他区域提供可复制的经验参考。

In the context of global climate governance and environmental pollution, carbon emissions, as the main source of greenhouse gas emissions, have become a key challenge restricting sustainable development. As a major industrial province and an economically strong province in the central and western regions of China, Shaanxi Province's low-carbon development path has important demonstration significance for the sustainable development of the region and even the whole country. This paper focuses on the influencing factors and scenario prediction of industrial carbon emissions in Shaanxi Province, aiming to provide scientific and reasonable countermeasures and suggestions for its low-carbon transition through systematic analysis.

On the basis of the concept of carbon emissions, the theory of low-carbon economy and the theory of sustainable development, this paper first analyzes the current situation of industrial development in Shaanxi Province, and finds that the industrial energy consumption structure of Shaanxi Province is relatively simple, and the energy intensity generally shows a fluctuating and decreasing trend, and the carbon emission coefficient method is used to measure the industrial carbon emissions from 2010 to 2023, showing a gradual increasing trend. Secondly, the gray correlation analysis, STIRPAT model and ridge regression method were used to analyze the influencing factors of industrial carbon emissions in Shaanxi Province, and the results of gray correlation analysis showed that among the ten initially selected indicators, in addition to the green coverage rate of built-up areas, consumer price index and the number of industrial enterprises, the remaining seven indicators: population size, per capita GDP, energy structure, energy intensity, urbanization rate, industrial added value, The correlation degree of industrial structure is more than 0.75, and there is a high correlation with carbon emissions, and the explanatory power of energy intensity and urbanization rate is prominent among many influencing factors. The results of ridge regression estimation show that population size, per capita GDP, energy structure, energy intensity, urbanization rate, industrial added value and industrial structure are significantly positively correlated with carbon emissions. Thirdly, this paper uses the scenario analysis method to construct three scenarios: the baseline scenario, the low-carbon scenario and the high-carbon scenario, and predicts the industrial carbon emissions of Shaanxi Province from 2024 to 2035 under different scenarios. Finally, the corresponding countermeasures and suggestions for industrial carbon emission reduction in Shaanxi Province are proposed, mainly including: (1) Optimizing the industrial energy structure; (2) Accelerate the upgrading of industrial structure; (3) Accelerate the progress and innovation of science and technology; (4) Strengthen ecological compensation and environmental monitoring; (5) Strengthen policy incentives and public participation; (6) Construction of ecological industrial parks.

In summary, on the one hand, this study has important practical significance for Shaanxi Province to promote low-carbon transformation and sustainable development, and through the scientific prediction and in-depth analysis of carbon emission scenarios, it can provide a key decision-making basis for Shaanxi Province to formulate low-carbon development goals and policy systems adapted to the provincial conditions, and on the other hand, its methodological framework and implementation path can also provide replicable experience reference for other regions.

[1] 张晓玲.可持续发展理论:概念演变、维度与展望[J].中国科学院院刊,2018,33(01):10-19.

[2] 李芳,王芮,陈玉博.新能源经济学研究进展[J].中国能源,2018,40(11):31-35+44.

[3] Gao Y, Gao X, Zhang X. The 2 C global temperature target and the evolution of the long-term goal of addressing climate change—from the United Nations framework convention on climate change to the Paris agreement[J]. Engineering, 2017, 3(2): 272-278.

[4] 宋杰鲲,梁璐璐,康忠燕.能耗、大气污染物排放及碳排放协同研究可视化分析[J].中外能源,2018,23(04):11-18.

[5] 熊林波,陈闻君.碳交易对低碳经济发展的影响及机制研究[J].企业经济,2024,43(02):66-78.

[6] 刘天蔚,边晓燕,吴珊等.电力系统碳排放核算综述与展望[J].电力系统保护与控制,2024,52(04):176-187.

[7] 吉红洁,袁进,郭亚兵等.关于焦化行业CO2排放核算方法的探讨[J].中国环境管理,2015,7(01):63-67.

[8] 郑玉蓉,孙文彬,杜守航等.煤炭企业碳排放核算方法研究综述[J/OL].煤炭学报,1-13[2024-03-10].

[9] Roberta, Q. and Sierra, P. The energy-climate challenge: Recent trends in CO2 emissions from fuel combustion[J]. Energy Policy,2007,35(11): 5938-5952.

[10] Hammond, G.P. and Norman, J.B. Decomposition analysis of energy-related carbon emissions from UK manufacturing[J].Energy, 2012,41(1): 220-227.

[11] 程叶青,王哲野,张守志,叶信岳,姜会明.中国能源消费碳排放强度及其影响因 素的空间计量[J].地理学报,2013,68(10):1418-1431.

[12] 孙雷刚,刘剑锋,徐全洪,王绍强,周蕾.环京津区域城市碳排放效应及时空格局 分析[J].地理与地理信息科学,2016,32(04):113-118.

[13] 宋杰鲲,牛丹平,曹子建,张凯新.中国省域碳排放测算及配额分配[J].技术经 济,2016,35(11):79-87.

[14] 王少剑,田莎莎,蔡清楠等.产业转移背景下广东省工业碳排放的驱动因素及碳转移分析[J].地理研究,2021,40(09):2606-2622.

[15] 李睿奇.碳达峰背景下安徽省工业碳排放与经济脱钩研究[J].现代工业经济和信息化,2024,14(01):63-66.

[16] 田慧慧.“双碳”背景下河南省旅游业碳排放测算与脱钩分析[J/OL].经营与管理,1-10[2024-03-10].

[17] Ehrlich P. R., Holden J. P. Impact of population growth[J]. Science, 1971, 171: 1212-1217.

[18] Ehrlich P. R., Holden J. P. One dimensional economy[J]. Bulletin of Atomic Scientists, 1972, 16: 18-27.

[19] Richard York, Eugene A Rosa, Thomas Dietz, STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts, Ecological Economics, Volume 46, Issue 3,2003, Pages 351-365.

[20] Ying Fan , Lan-Cui Liu , Gang Wu , Yi-Ming Wei. Analyzing impact factors of CO2 emissions using the STIRPAT model .Environmental Impact Assessment Review，2006（26）: 377– 395.

[21] Diakoulaki D, Mavrotas G, Oekopoulos D, et al. A bottom-up decomposition analysis of energy-related CO2 emissions in Greece[J].Energy, 2006,31(14):2638-2651.

[22] Richard King. Carbon Dioxide Emissions, Technology, and Economic Growth[J]. Poverty to Power Research Papers, 2008.

[23] 朱勤,彭希哲,陆志明等.人口与消费对碳排放影响的分析模型与实证[J].中国人口·资源与环境,2010,20(02):98-102.

[24] 赵欣,龙如银.江苏省碳排放现状及因素分解实证分析[J].中国人口·资源与环 境,2010,119(7): 25-30.

[25] Liddle, B., Lung, S. Age-structure, urbanization, and climate change in developed countries: revisiting STIRPAT for disaggregated population and consumption-related environmental impacts. Popul Environ 31, 317–343 (2010).

[26] Butnar I, Llop M. Structural decomposition analysis and input-output subsystems: Changesin CO2 emissions of Spanish service sectors (2000-2005)[J].Ecological Economics,2011.70(11): 2012-2019.

[27] Brizga J, Feng K, Hubacek K. Drivers of greenhouse gas emissions in the Baltic States: A structural decomposition analysis[J].Ecological Economics,2014,98(2):22-28.

[28] 孙欣,张可蒙.中国碳排放强度影响因素实证分析[J] .统计研究,2014,31(2):61-67.

[29] Das A, Paul S K.CO2 emissions from household consumption in India between 1993-94 and 2006-07:A decomposition analysis[J].Energy Economics,2014,41(1): 90-105.

[30] Wang H, Aang B W, Su B.A Multi-region Structural Decomposition Analysis of Global CO2 Emission Intensity[J].Ecological Economics,2017,142:163-176.

[31] 薛黎明,张心智,刘保康等.基于支持向量回归机的河北省能源消费碳排放预测[J].煤炭工程, 2017,49(08):165-168.

[32] 张晨露,张凡.生态保护、产业结构升级对碳排放的影响—基于长江经济带数据的实证[J].统计与决策,2022,38(03):77-80.

[33] 赵江燕,朱宇恩,马建超等.山西省能源消费碳排放清单和影响因素研究[J].太原理工大学学报,2022,53(06):989-996.

[34] 牛乐,张丽霞,郗凤明等.辽宁省碳排放影响因素及情景预测[J].应用生态学报,2023,34(02):499-509.

[35] 梁潆月,李兰英.农业碳排放的EKC检验及影响因素研究—以长三角地区为例[J].福建农业科技, 2022, 53(01):72-80.

[36] 何乐天,杨泳琪,李蓉等.基于STIRPAT模型的黑龙江省工业碳排放情景分析与峰值预测[J/OL].资源与产业,1-11.

[37] 吴辉,李雪曼,岳枫等.未来不同情景下发电企业碳排放量的预估[J/OL].人民长江,1-11.

[38] 潘思羽,张美玲.基于BP神经网络的甘肃省二氧化碳排放预测及影响因素研究[J].环境工程,2023,41(07):61-68+85.

[39] 王航星,董洪光,罗涛等.安徽省工业碳排放与经济增长的相关性分析——基于EKC模型和Tapio模型[J].西安文理学院学报(自然科学版),2024,27(01):68-75.

[40] 王红平,刘凯.福建省造纸工业碳排放的灰色关联度、Tapio脱钩分析及情景预测[J/OL].中国造纸学报,1-8.

[41] 李清瑶,张勇,胡民泉等.“双碳”目标实现的省域实践：以浙江为例[J].生态与农村环境学报,2024,40(02):191-204.

[42] 石振武,毕爱琦,王金茹.京津冀建筑业碳排放区域差异及影响因素研究[J/OL].工程管理学报,1-6.

[43] Sun M ,Fu Y ,Sun M , et al.Study on Key Influencing Factors of Carbon Emissions from Farmland Resource Utilization in Northeast China Under the Background of Energy Conservation and Emission Reduction[J].Energies,2025,18(2):277-277.

[44] 马艺鸣,李国平.中国降碳减污扩绿协同水平、区域差异及影响因素[J/OL].统计与决策,2025,(05):103-108.

[45] 欧阳强,李奇.湖南省碳排放影响因素的灰色关联分析与预测[J].长沙理工大学学报(社会科学版),2012,27(01):65-69.

[46] 纪广月.基于灰色关联分析的BP神经网络模型在中国碳排放预测中的应用[J].数学的实践与认识,2014,44(14):243-249．

[47] 王永哲,马立平.吉林省能源消费碳排放相关影响因素分析及预测—基于灰色关联分析和GM(1,1)模型[J].生态经济,2016,32(11):65-70.

[48] Wenqing Wu , Xin Ma , Yuanyuan Zhang, Wanpeng Li, Yong Wang. A novel conformable fractional non-homogeneous grey model for forecasting carbon dioxide emissions of BRICS countries [J]. Sci Total Environ, 2020, 707: 135447.

[49] 范德成,张修凡.基于PSO-BP神经网络模型的中国碳排放情景预测及低碳发展路径研究[J].中外能源,2021,26(08):11-19.

[50] 陈喜阳,周程,王田.多情景视角下中国能源消费和碳达峰路径[J].环境科学,2023,44(10):5464-5477.

[51] 丘康尧,何寿奎.广东省建筑业碳排放影响因素与碳达峰路径：基于STIRPAT和岭回归[J/OL].河南科学,1-10.

[52] 王火根,汪钰婷,肖丽香.基于IO-SDA法的2020～2060年中国行业CO2排放预测与分析[J].中国环境科学,2024,44(03):1743-1755.

[53] 林伯强,杜克锐.理解中国能源强度的变化:一个综合的分解框架[J].世界经济,2014,37(04):69-87.

[54] 李强,左静娴.基于STIRPAT模型的长江经济带碳排放峰值预测研究[J].东北农业大学学报(社会科学版),2017,15(05):53-58.

[55] 席细平,涂梦姿,孙李媛等.江西省重点行业碳减排潜力分析与实施路径[J].能源研究与管理,2023,(02):1-7+18.

[56] Hao W ,Yi Y ,Wen L .Analysis of spatiotemporal evolution characteristics and peak forecast of provincial carbon emissions under the dual carbon goal: Considering nine provinces in the Yellow River basin of China as an example[J].Atmospheric Pollution Research,2023,14(8):101828

[57] 杨东,李艳红,田春林.交通运输行业碳排放自然达峰特征与峰值预测研究[J].交通运输系统工程与信息,2024,24(02):34-44.

[58] Cai T, Li Y, Wang P, et al. A Taguchi-STIRPAT input–output model for unveiling the pathways of reducing household carbon emissions under dual-carbon target—A case study of Fujian province[J]. Environmental Science and Pollution Research, 2024, 31(10): 15424-15442.

[59] Xia X ,Liu B ,Wang Q , et al.Analysis of carbon peak achievement at the provincial level in China: Construction of ensemble prediction models and Monte Carlo simulation[J].Sustainable Production and Consumption,2024,50445-461.

[60] Wang H ,Wei Z ,Fang T , et al.Carbon emissions prediction based on the GIOWA combination forecasting model: A case study of China[J].Journal of Cleaner Production,2024,445141340.

[61] Zhao B ,Yang W .Carbon emissions forecasting based on a new hybrid model under carbon reduction target: A case study of eastern region in China[J].Journal of Cleaner Production,2025, 144957.

[62] Miraftabzdeh M S ,Khan A M ,Bayati N , et al.Deep learning methods and evaluation of the extensive carbon emission predictive solution for Danish grid[J].Sustainable Energy Technologies and Assessments,2025,75104242-104242.

[63] 李国平,马艺鸣,曾先峰.国家重点生态功能区绿色发展水平评价:以陕西省为例[J].统计与决策,2024,40(04):129-133.

[64] 马诗萍,谢永顺,陈宏阳,等.中国城市电力生产总碳强度的时空演变及影响因素[J].地理学报,2024,79(03):712-731.

[65] 赖晓瑾,唐一丁,祝树金.各国参与全球价值链的环境库兹涅茨效应—基于跨国面板数据[J].资源科学,2022,44(08):1630-1644.

[66] 张亚博.基于STIRPAT-LEAP模型的京津冀区域碳排放峰值预测[D].中国石油大学(北京),2021.

[67] 石俊,张曦予,代薪宇,等.长江上游经济带碳减排与高质量发展耦合协调研究[J].统计与决策,2024,40(17):90-94.

[68] 杨刚强,王海森,范恒山,等.数字经济的碳减排效应：理论分析与经验证据[J].中国工业经济,2023,(05):80-98.

[69] 黄德春,沈雪梅,竺运.长江经济带制造业碳排放效率的时空演变及影响因素研究[J].长江流域资源与环境,2023,32(06):1113-1126.

[70] 何淼,张阳,栾韶涵,等.基于LMDI-Tapio-GA-BP的中国城乡居民消费碳排放分析[J].华南师范大学学报(自然科学版),2024,56(06):1-16.