在全球气候变化加剧和“双碳”目标深入推进的背景下，工业碳排放问题已成为制约经济高质量发展的重要瓶颈。根据2023年国际能源署（IEA）数据显示，中国工业领域碳排放占全国总量比例长期维持在65%以上，其中西部地区作为国家能源战略储备基地和重化工业集聚区，承担着全国近40%的能源输出和25%的工业增加值。这种“高碳锁定”的发展模式不仅加剧了区域生态环境恶化，更与生态文明建设和“美丽中国”战略目标形成突出矛盾。特别是在“一带一路”建设纵深推进和新时代西部大开发格局加速形成的双重背景下，破解西部地区工业发展与碳减排的协同困境，成为实现区域可持续发展必须解决的重要课题。

本文以可持续发展理论为指导，融合创新、协调、绿色、开放、共享的新发展理念，并基于投入产出理论构建西部地区工业碳排放效率评价指标体系，聚焦于2010年至2021年的12年数据区间，跨越了全球经济和能源结构转型的关键节点。利用这一广泛的时间序列数据，选取西部地区11个省份的投入产出面板数据，采用三阶段DEA模型从总体和区域两层面出发测算非传统地理划分下的西部地区工业碳排放效率，并进行效率分解，以解释不同低效率水平的驱动力量，同时深入考察了9个环境因素对工业碳排放效率的影响。最后采用Dagum基尼系数及其分解方法和Kernel密度估计方法，揭示西部地区及西部三区域工业碳排放效率的区域差异及来源，并直观地展示了不同时间点工业碳排放效率的分布格局和演变过程。

研究结果表明：（1）剔除环境和随机因素后，西部地区工业碳排放效率有所提升，但存在区域间差异，表现为“第三区域>第二区域>第一区域”的特点；（2）绿色发展水平、共享发展水平、创新发展水平和协调发展水平对西部地区工业碳排放效率的提升有积极影响；工业化水平影响较小；而经济发展、政府支持、开放发展水平和能源消费结构尚未发挥显著作用；（3）西部地区工业碳排放效率的空间差异在样本期内总体呈增大态势，地区间差异为空间差异的主要来源；（4）西部地区工业碳排放效率在时空上呈现出整体提高但存在阶段性差异和地区差异多极分化的特点。针对以上研究结论，本文从总体和区域层面提出推动西部地区工业碳排放效率提升的对策建议，以期推动西部地区工业碳排放效率的持续提升，为促进工业低碳转型，实现区域经济与节能减排的协调发展做出积极贡献。

Under the intensifying global climate crisis and China's deepening pursuit of "dual carbon" targets, industrial carbon emissions have emerged as a critical bottleneck constraining high-quality economic development. According to the 2023 data from the International Energy Agency (IEA), China's industrial sector accounts for over 65% of national carbon emissions. Notably, the western region—serving as a national energy strategic reserve base and a hub for heavy and chemical industries—contributes 40% of the country's energy output and 25% of the industrial value-added. This entrenched "high-carbon lock-in" development model not only exacerbates regional ecological degradation but also directly conflicts with China's strategic goals of ecological civilization construction and the "Beautiful China" initiative. Against the backdrop of the advancing implementation of the Belt and Road Initiative and the accelerated formation of a new pattern for the development of China's western regions in the new era, addressing the synergy dilemma between industrial development and carbon reduction in the western region has become an imperative issue for achieving regional sustainable development.

Guided by the theory of sustainable development and integrating the new development paradigm of innovative, coordinated, green, open, and inclusive development, this study constructs an industrial carbon emission efficiency evaluation system based on the input-output theory, focusing on a 12-year data span from 2010 to 2021, which spans key junctures in global economic and energy structural transformation. Using input-output panel data from 11 provinces in the western region, the study employs a three-stage Data Envelopment Analysis (DEA) model to measure industrial carbon emission efficiency under non-traditional geographical classifications from both overall and regional perspectives. Efficiency decomposition is conducted to explain the driving forces behind different levels of inefficiency, while an in-depth examination is performed on the impact of nine environmental factors. Furthermore, the Dagum Gini coefficient decomposition method combined with kernel density estimation is applied to reveal regional disparities, their sources, and the distribution patterns and evolutionary processes of industrial carbon emission efficiency.

The research findings indicate that: (1) After adjusting for environmental and random factors, industrial carbon emission efficiency demonstrates improvement in the western region, yet exhibits inter-regional differences characterized by Third Region > Second Region > First Region. (2) Enhancements in green development, shared development, innovative development, and coordinated development significantly improve industrial carbon emission efficiency, whereas industrialization exerts minor impacts. Economic development, government support, open development levels, and energy consumption structure show no statistically significant effects. (3) Spatial disparities in industrial carbon emission efficiency exhibit an increasing trend during the sample period, primarily attributable to inter-regional differences. (4) While industrial carbon emission efficiency shows overall spatiotemporal improvement, phased disparities and multipolarization patterns persist in regional differences. Based on these findings, this study proposes targeted countermeasures from holistic and regional perspectives to enhance industrial carbon efficiency in the western region, aiming to promote industrial low-carbon transformation and achieve coordinated development between the regional economy and energy conservation and emission reduction.

[1] Song F, Zhang J G, Ramanathan V, et al. Trends in surface equivalent potential temperature: A more comprehensive metric for global warming and weather extremes[J]. Proceedings of the National Academy of Sciences, 2022, 119(6):1-7.

[2] 中华人民共和国2022年国民经济和社会发展统计公报[J].中国统计,2023(03):12-29.

[3] Kaya Y, Yokobori K. Environment, Energy, and Economy: Strategies for Sustainability[M]. Delhi:Bookwell Publications, 1999.

[4] Mielnik O, Goldemberg J. Communication The evolution of the "carbonization index" in developing countries[J]. Energy Policy, 1999, 27(5):307-308.

[5] Sun J W. The decrease of CO2 emission intensity is decarbonization at national and global levels[J]. Energy Policy, 2005, 33(8): 975-978.

[6] 潘家华,张丽峰.我国碳生产率区域差异性研究[J].中国工业经济,2011(5):47-57.

[7] 唐志鹏,刘卫东,宋涛.基于混合地理加权回归的中国省域碳生产率影响因素分析[J].资源科学,2017,39(12):2223-2232.

[8] Parker S, Bhatti M I. Dynamics and drivers of per capita CO2 emissions in Asia[J]. Energy Economics, 2020, 89:104798.

[9] Zhang R J, Tai H W, Cheng K T, et al. Carbon emission efficiency network formation mechanism and spatial correlation complexity analysis: Taking the Yangtze River Economic Belt as an example[J]. Science of the Total Environment, 2022, 841:156719.

[10] Ramanathan R. Combining indicators of energy consumption and CO2 emissions: across-country comparison[J]. International Journal of Global Energy Issues, 2002, 17(3):214-227.

[11] Zaim O, Taskin F. Environmental efficiency in carbon dioxide emissions in the OECD: A non-parametric approach[J]. Journal of Environmental Management, 2000, 58(2):95-107.

[12] 韩晶,王赟,陈超凡.中国工业碳排放绩效的区域差异及影响因素研究——基于省域数据的空间计量分析[J].经济社会体制比较,2015,(01):113-124.

[13] 刘康,袁敏,申社芳.我国区域碳排放效率测度与地区差异分析——基于三阶段Super-SBM-DEA方法[J].兰州财经大学学报,2022,38(02):44-59.

[14] 张悦,王晶晶,程钰.中国工业碳排放绩效时空特征及技术创新影响机制[J].资源科学,2022,44(07):1435-1448.

[15] Molinos-Senante M, Sala-Garrido R, Hernández-Sancho F. Development and application of the Hicks-Moorsteen productivity index for the total factor productivity assessment of wastewater treatment plants[J]. Journal of Cleaner Production, 2016, 112:3116-3123.

[16] 余敦涌,张雪花,刘文莹.基于随机前沿分析方法的碳排放效率分析[J].中国人口·资源与环境,2015,25(S2):21-24.

[17] 张丽峰.碳排放约束下中国全要素生产率测算与分解研究——基于随机前沿分析(SFA)方法[J].干旱区资源与环境,2013,27(12):20-24.

[18] 郭茹,吕爽,曹晓静等.基于ZSG-DEA模型的中国六大行业碳减排分配效率研究[J].生态经济,2020,36(01):13-18.

[19] 曾诗鸿,徐妍.基于零和DEA模型的中国省级碳减排分配效率研究[J].管理现代化,2014,34(05):63-65.

[20] Huang H, Wang T. The total-factor energy efficiency of regions in China: based on three-stage SBM model[J]. Sustainability, 2017, 9(9):1664.

[21] 彭文斌,曹笑天,苏昌贵等.长江中游城市群碳效率时空演化特征——基于三阶段SBM-DEA模型[J].生态学报,2023,43(09):3532-3545.

[22] 陈进,陶恩泽,陶红等.宁夏沿黄城市带耕地碳排放时空特征和生态效率评价[J].水土保持通报,2024,44(03):418-429.

[23] Li J, Ma J, Wei W. Analysis and evaluation of the regional characteristics of carbon emission efficiency for China[J]. Sustainability, 2020, 12(8):3138.

[24] Wang Y, Duan F, Ma X, et al. Carbon emissions efficiency in China: Key facts from regional and industrial sector[J]. Journal of Cleaner Production, 2019, 206:850-869.

[25] Marklund P O, Samakolis E. What is driving the EU burden sharing agreement: Efficiency or equity?[J]. Journal of Environmental Management, 2007, 85(2):317-329.

[26] Zaim O, Taskin F. Environmental efficiency in carbon dioxide emissions in the OECD: A non-parametric approach[J]. Journal of Environmental Management, 2000, 58(2):95-107.

[27] Zofı́o J L, Prieto A M. Environmental efficiency and regulatory standards: the case of CO2 emissions from OECD industries[J]. Resource and Energy Economics, 2001, 23(1):63-83.

[28] Zhou P, Ang B W, Han J Y. Total factor carbon emission performance: A Malmquist index analysis[J]. Energy Economics, 2010, 32(1):194-201.

[29] Liu C, Tang R, Guo Y, et al. Research on the structure of carbon emission efficiency and influencing factors in the Yangtze River Delta urban agglomeration[J]. Sustainability, 2022, 14(10):6114.

[30] 米莹,蔺雪芹,王岱等.中国东部沿海地区工业碳排放效率时空演化特征及影响因素[J].西安理工大学学报,2022,38(04):476-486.

[31] 李健,马晓芳,苑清敏.区域碳排放效率评价及影响因素分析[J].环境科学学报,2019,39(12):4293-4300.

[32] Jiang H, Yin J. Strategies for improving the industrial carbon emission efficiency in China: an approach based on trend prediction and regional learning mechanism[J]. Environmental Science and Pollution Research, 2023, 30(57):120188-120206.

[33] 马大来,武文丽,董子铭.中国工业碳排放绩效及其影响因素——基于空间面板数据模型的实证研究[J].中国经济问题,2017,(01):121-135.

[34] Jun L, Lu S, Li X, et al. Spatio-temporal characteristics of industrial carbon emission efficiency and their impacts from digital economy at Chinese prefecture-level cities[J]. Sustainability, 2023, 15(18):13694.

[35] 尹剑,龙垚垚,姜洪涛.珠江流域城市工业碳排放效率的空间关联网络特征与影响因素[J].环境科学,2024,45(12):6806-6817.

[36] 王兆峰,杜瑶瑶.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,2019,39(05):797-806.

[37] Zhu R, Zhao R, Sun J, et al. Temporospatial pattern of carbon emission efficiency of China’s energy-intensive industries and its policy implications[J]. Journal of cleaner production, 2021, 286:125507.

[38] Lin B, Chen X. Evaluating the CO2 performance of China's non-ferrous metals Industry: A total factor meta-frontier Malmquist index perspective[J]. Journal of Cleaner Production, 2019, 209:1061-1077.

[39] 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, 274:123168.

[40] Han Y, Geng Z, Zhu Q, et al. Energy efficiency analysis method based on fuzzy DEA cross-model for ethylene production systems in chemical industry[J]. Energy, 2015, 83:685-695.

[41] Wu P, Wang Y, Chiu Y, et al. Production efficiency and geographical location of Chinese coal enterprises-undesirable EBM DEA[J]. Resources Policy, 2019, 64:101527.

[42] 屈秋实,王礼茂,王博等.中国有色金属产业链碳排放及碳减排潜力省际差异[J].资源科学,2021,43(04):756-763.

[43] Kwakwa P A, Adusah-Poku F. The carbon dioxide emission effects of domestic credit and manufacturing indicators in South Africa[J]. Management of Environmental Quality: An International Journal, 2020, 31(6):1531-1548.

[44] Benjamin N I, Lin B. Quantile analysis of carbon emissions in China metallurgy industry[J]. Journal of Cleaner Production, 2020, 243:118534.

[45] Yang Y, Yuan Z, Yang S. 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.

[46] 李强,魏巍.碳排放约束视角下的全要素能源效率及影响因素研究[J].软科学,2015,29(04):71-74.

[47] Pei Y, Zhu Y, Wang N. How do corruption and energy efficiency affect the carbon emission performance of China’s industrial sectors?[J]. Environmental Science and Pollution Research, 2021, 28:31403-31420.

[48] 韩川.技术进步对中国工业碳排放的影响分析[J].大连理工大学学报(社会科学版),2018,39(02):65-73.

[49] 仲云云,仲伟周.中国区域全要素碳排放绩效及影响因素研究[J].商业经济与管理,2012,(01):85-96.

[50] 边宇,蔺雪芹,周笑等.京津冀工业碳排放时空演化特征及影响因素[J].环境科学与技术,2021,44(11):37-47.

[51] 余红伟,林子祥,胡力元等.高质量发展下中国工业企业碳减排路径选择[J].中国软科学,2024,(01):214-224.

[52] 王惠,卞艺杰,王树乔.出口贸易、工业碳排放效率动态演进与空间溢出[J].数量经济技术经济研究,2016,33(01):3-19.

[53] 周杰琦,韩颖,林洪.FDI对中国工业碳排放效率的影响机理及其效应——理论构建与经验分析[J].软科学,2016,30(01):76-80.

[54] 宁论辰,郑雯,曾良恩.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),2021,57(01):181-188.

[55] 雷玉桃,彭文祥,张萱.工业数字化转型的碳减排效应研究——来自中国行业的经验证据[J].经济经纬,2024,41(01):97-109.

[56] Wang K, Wu M, Sun Y, et al. Resource abundance, industrial structure, and regional carbon emissions efficiency in China[J]. Resources Policy, 2019, 60:203-214.

[57] 陈前利,马贤磊,石晓平等.工业用地供应行为影响工业能源碳排放吗?——基于供应规模、方式与价格三维度分析[J].中国人口·资源与环境,2019,29(12):57-67.

[58] 吕斌,王科,徐志强.针对能源效率问题的中国地区聚类[J].经济地理,2013,33(04):15-21.

[59] 王雪,刘冠权,张瑜.黄河流域工业碳排放绩效测度及影响因素研究[J].鲁东大学学报(自然科学版),2025,41(01):62-71.

[60] 华坚,任俊,徐敏等.基于三阶段DEA的中国区域二氧化碳排放绩效评价研究[J].资源科学,2013,35(07):1447-1454.

[61] Seiford L M, Zhu J. Modeling undesirable factors in efficiency evaluation[J]. European Journal of Operational Research, 2002, 142(1):16-20.

[62] Fried H O, Lovell C A K, Schmidt S S, et al. Accounting for environmental effects and statistical noise in data envelopment analysis[J]. Journal of productivity Analysis, 2002, 17:157-174.

[63] 王弘儒.中国城乡高质量融合发展水平的地区差距及分布动态演进[J].经济问题探索,2023,(02):45-64.

[64] 黄璐,张红凤.中国营商环境评价及时空演化特征分析[J].统计与决策,2023,39(16):157-162.