Urban Industrial Carbon Efficiency Measurement and Influencing Factors Analysis in China
Based on the EBM-DEA (Explainable Boosting Machine-Data Envelopment Analysis) model, this paper constructs an evaluation model of urban industrial carbon efficiency (UICE), measures and analyzes the spatial evolution characteristics of China’s UICE from 2003 to 2016, and analyzes the influencing fac...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/12/1/26 |
| _version_ | 1797440013588234240 |
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| author | Weijia Cui Xueqin Lin Dai Wang Ying Mi |
| author_facet | Weijia Cui Xueqin Lin Dai Wang Ying Mi |
| author_sort | Weijia Cui |
| collection | DOAJ |
| description | Based on the EBM-DEA (Explainable Boosting Machine-Data Envelopment Analysis) model, this paper constructs an evaluation model of urban industrial carbon efficiency (UICE), measures and analyzes the spatial evolution characteristics of China’s UICE from 2003 to 2016, and analyzes the influencing factors of UICE using the Tobit model. The research draws the following conclusions: (1) China’s UICE improved from 2003 to 2016, and the distribution showed a spatial pattern decreasing from the east, central, west, and northeast regions. (2) The UICE, by region, was at an initial low stable level in 2003 and was in the process of moving towards a highly-efficient stable state up until 2016. The differences between regions have been the main aspect which affects the overall variation in UICE in China. (3) There is a logistic curve relationship between the economic development level and UICE. (4) Nationally, the factors that are significantly and positively correlated with UICE are: industrial agglomeration, local fiscal decentralisation, level of economic development, technological progress, industrial enterprises’ average size, and industrial diversification. Factors that are significantly negatively correlated with UICE are the level of industrialization, the share of output value of state-owned enterprises in total output value, industrial openness, and environmental regulation. The factors influencing UICE differ depending on the stage of industrialization. |
| first_indexed | 2024-03-09T12:02:00Z |
| format | Article |
| id | doaj.art-b1d07c1971f14e60863981c124c8daba |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-09T12:02:00Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-b1d07c1971f14e60863981c124c8daba2023-11-30T23:03:18ZengMDPI AGLand2073-445X2022-12-011212610.3390/land12010026Urban Industrial Carbon Efficiency Measurement and Influencing Factors Analysis in ChinaWeijia Cui0Xueqin Lin1Dai Wang2Ying Mi3College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, ChinaCollege of Resource Environment and Tourism, Capital Normal University, Beijing 100048, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaBased on the EBM-DEA (Explainable Boosting Machine-Data Envelopment Analysis) model, this paper constructs an evaluation model of urban industrial carbon efficiency (UICE), measures and analyzes the spatial evolution characteristics of China’s UICE from 2003 to 2016, and analyzes the influencing factors of UICE using the Tobit model. The research draws the following conclusions: (1) China’s UICE improved from 2003 to 2016, and the distribution showed a spatial pattern decreasing from the east, central, west, and northeast regions. (2) The UICE, by region, was at an initial low stable level in 2003 and was in the process of moving towards a highly-efficient stable state up until 2016. The differences between regions have been the main aspect which affects the overall variation in UICE in China. (3) There is a logistic curve relationship between the economic development level and UICE. (4) Nationally, the factors that are significantly and positively correlated with UICE are: industrial agglomeration, local fiscal decentralisation, level of economic development, technological progress, industrial enterprises’ average size, and industrial diversification. Factors that are significantly negatively correlated with UICE are the level of industrialization, the share of output value of state-owned enterprises in total output value, industrial openness, and environmental regulation. The factors influencing UICE differ depending on the stage of industrialization.https://www.mdpi.com/2073-445X/12/1/26urban industrial carbon efficiency (UICE)spatial evolution characteristicsinfluencing factorsEBM modelTobit modelChina |
| spellingShingle | Weijia Cui Xueqin Lin Dai Wang Ying Mi Urban Industrial Carbon Efficiency Measurement and Influencing Factors Analysis in China Land urban industrial carbon efficiency (UICE) spatial evolution characteristics influencing factors EBM model Tobit model China |
| title | Urban Industrial Carbon Efficiency Measurement and Influencing Factors Analysis in China |
| title_full | Urban Industrial Carbon Efficiency Measurement and Influencing Factors Analysis in China |
| title_fullStr | Urban Industrial Carbon Efficiency Measurement and Influencing Factors Analysis in China |
| title_full_unstemmed | Urban Industrial Carbon Efficiency Measurement and Influencing Factors Analysis in China |
| title_short | Urban Industrial Carbon Efficiency Measurement and Influencing Factors Analysis in China |
| title_sort | urban industrial carbon efficiency measurement and influencing factors analysis in china |
| topic | urban industrial carbon efficiency (UICE) spatial evolution characteristics influencing factors EBM model Tobit model China |
| url | https://www.mdpi.com/2073-445X/12/1/26 |
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