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Yu W, Xia L, Cao Q. A machine learning algorithm to explore the drivers of carbon emissions in Chinese cities. Sci Rep 2024; 14:23609. [PMID: 39384880 PMCID: PMC11464641 DOI: 10.1038/s41598-024-75753-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Accepted: 10/08/2024] [Indexed: 10/11/2024] Open
Abstract
As the world's largest energy consumer and carbon emitter, the task of carbon emission reduction is imminent. In order to realize the dual-carbon goal at an early date, it is necessary to study the key factors affecting China's carbon emissions and their non-linear relationships. This paper compares the performance of six machine learning algorithms to that of traditional econometric models in predicting carbon emissions in China from 2011 to 2020 using panel data from 254 cities in China. Specifically, it analyzes the comparative importance of domestic economic, external economic, and policy uncertainty factors as well as the nonparametric relationship between these factors and carbon emissions based on the Extra-trees model. Results show that energy consumption (ENC) remains the root cause of increased carbon emissions among domestic economic factors, although government intervention (GOV) and digital finance (DIG) can significantly reduce it. Next, among the external economic and policy uncertainty factors, foreign direct investment (FDI) and economic policy uncertainty (EPU) are important factors influencing carbon emissions, and the partial dependence plots (PDPs) confirm the pollution haven hypothesis and also reveal the role of EPU in reducing carbon emissions. The heterogeneity of factors affecting carbon emissions is also analyzed under different city sizes, and it is found that ENC is a common driving factor in cities of different sizes, but there are some differences. Finally, appropriate policy recommendations are proposed by us to help China move rapidly towards a green and sustainable development path.
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Affiliation(s)
- Wenmei Yu
- School of Finance, Anhui University of Finance and Economics, Bengbu, 233030, China
| | - Lina Xia
- School of Finance, Anhui University of Finance and Economics, Bengbu, 233030, China
| | - Qiang Cao
- School of Finance, Anhui University of Finance and Economics, Bengbu, 233030, China.
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Chen F, Liu Y, Li R. Low-carbon development path based on carbon emission accounting and carbon emission performance evaluation: a case study of Chinese coal production enterprises. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:45522-45536. [PMID: 38967848 DOI: 10.1007/s11356-024-34133-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 06/22/2024] [Indexed: 07/06/2024]
Abstract
Carbon emission accounting is the basic premise of effective carbon emission reduction and management. This study aimed to establish the carbon emission model and performance evaluation framework of coal mine production enterprises and clarify the low-carbon development path of enterprises. In this study, we took a typical coal production enterprise (K enterprise) in the Shanxi province of China as the research object. We also estimated the carbon emissions of the enterprise mainly according to the Chinese Carbon Emission Accounting Standard (GB/T 32151.11-2018). The triangular model was used to construct the carbon performance evaluation framework. On this basis, we suggested the enterprise's low-carbon development path. The results showed that (1) the carbon emission of K enterprise in 2021 was 36,875.38 tCO2eq; the carbon emission intensity of each ton of coal produced was 0.089 tCO2eq. The critical carbon emissions were electricity consumption and methane fugitive emissions during production. (2) The evaluation indicators for carbon emission performance revealed an imbalance in K enterprise's economic, energy, and environmental development in 2021. The work on energy saving and consumption reduction was relatively weak. (3) Countermeasures for low-carbon development, including a carbon emission ledger, were proposed based on carbon emission accounting and performance evaluation results. This study can help typical underground coal production enterprises in Shanxi province obtain more accurate carbon emission data, providing practical guidance and reference for the same underground coal production enterprises to improve the carbon emission control effect.
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Affiliation(s)
- Fan Chen
- Institute of Environmental Science, Shanxi University, 63 South Central Ring Street, Taiyuan, 030006, China
- School of Environment and Resource, Shanxi University, Taiyuan, 030006, China
| | - Yang Liu
- Institute of Environmental Science, Shanxi University, 63 South Central Ring Street, Taiyuan, 030006, China
| | - Ruijin Li
- Institute of Environmental Science, Shanxi University, 63 South Central Ring Street, Taiyuan, 030006, China.
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Xu Y, Lin T, Du P, Wang J. The research on a novel multivariate grey model and its application in carbon dioxide emissions prediction. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:21986-22011. [PMID: 38400970 DOI: 10.1007/s11356-024-32262-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 01/26/2024] [Indexed: 02/26/2024]
Abstract
Accurate small-sample prediction is an urgent, very difficult, and challenging task due to the quality of data storage restricted in most realistic situations, especially in developing countries. The grey model performs well in small-sample prediction. Therefore, a novel multivariate grey model is proposed in this study, called FBNGM (1, N, r), with a fractional order operator, which can increase the impact of new information and background value coefficient to achieve high prediction accuracy. The utilization of an intelligence optimization algorithm to tune the parameters of the multivariate grey model is an improvement over the conventional method, as it leads to superior accuracy. This study conducts two sets of numerical experiments on CO2 emissions to evaluate the effectiveness of the proposed FBNGM (1, N, r) model. The FBNGM (1, N, r) model has been shown through experiments to effectively leverage all available data and avoid the problem of overfitting. Moreover, it can not only obtain higher prediction accuracy than comparison models but also further confirm the indispensable importance of various influencing factors in CO2 emissions prediction. Additionally, the proposed FBNGM (1, N, r) model is employed to forecast CO2 emissions in the future, which can be taken as a reference for relevant departments to formulate policies.
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Affiliation(s)
- Yan Xu
- Ocean University of China, Qingdao, 266100, China
- Qingdao Financial Research Institute, Qingdao, 266100, China
| | - Tong Lin
- Ocean University of China, Qingdao, 266100, China
| | - Pei Du
- School of Business, Jiangnan University, Wuxi, 214122, China.
- Food Safety Research Base of Jiangsu Province, Jiangnan University, Wuxi, 214122, China.
| | - Jianzhou Wang
- Institute of Systems Engineering, Macau University of Science and Technology, Macau, 999078, China
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Yuan B, Zhong Y, Li S, Zhao Y. The degree of population aging and living carbon emissions: Evidence from China. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 353:120185. [PMID: 38301479 DOI: 10.1016/j.jenvman.2024.120185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 12/12/2023] [Accepted: 01/20/2024] [Indexed: 02/03/2024]
Abstract
Population aging and global warming have become everyday concerns of all countries. Based on the panel data of 30 provinces in China from 2003 to 2019, this paper uses the panel fixed effect model and two-stage least square method to analyze the effect of population aging on domestic energy carbon emissions of urban and rural residents. On this basis, the threshold regression model is introduced to explore the heterogeneity of the effect under different aging levels. The results show that (1) the progress of population aging at the overall level will significantly increase the level of carbon emissions from household energy consumption. At the regional level, the effect of population aging on carbon emissions from household energy consumption in rural areas is higher than in urban areas. (2) Population aging has a nonlinear effect on the carbon emissions of residential energy consumption. For urban areas, when the level of population aging crosses the threshold, its marginal impact on living carbon emissions in urban areas is further enhanced. In contrast, the opposite is true in rural areas. (3) Heterogeneity analysis results show that the impact of population aging on residential energy carbon emissions differs in different regions at the national and rural levels but does not show regional heterogeneity at the urban level.
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Affiliation(s)
- Bin Yuan
- School of Management, Ocean University of China, Qingdao, 266100, PR China
| | - Yuping Zhong
- School of Management, Ocean University of China, Qingdao, 266100, PR China
| | - Shengsheng Li
- School of Management, Ocean University of China, Qingdao, 266100, PR China.
| | - Yihang Zhao
- School of Management, Ocean University of China, Qingdao, 266100, PR China
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Fang W, Luo P, Luo L, Zha X, Nover D. Spatiotemporal characteristics and influencing factors of carbon emissions from land-use change in Shaanxi Province, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:123480-123496. [PMID: 37987976 DOI: 10.1007/s11356-023-30606-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 10/15/2023] [Indexed: 11/22/2023]
Abstract
Due to global warming, there evolves a global consensus and urgent need on carbon emission mitigations, especially in developing countries. We investigated the spatiotemporal characteristics of carbon emissions induced by land use change in Shaanxi at the city level, from 2000 to 2020, by combining direct and indirect emission calculation methods with correction coefficients. In addition, we evaluated the impact of 10 different factors through the geodetector model and their spatial heterogeneity with the geographic weighted regression (GWR) model. Our results showed that the carbon emissions and carbon intensity of Shaanxi had increased overall in the study period but with a decreased growth rate during each 5-year period: 2000-2005, 2005-2010, 2010-2015, and 2015-2020. In terms of carbon emissions, the conversion of croplands into built-up land contributed the most. The spatial distribution of carbon emissions in Shaanxi was ranked as follows: Central Shaanxi > Northern Shaanxi > Southern Shaanxi. Local spatial agglomeration was reflected in the cold spots around Xi'an, and hot spots around Yulin. With respect to the principal driving factors, the gross domestic product (GDP) was the dominant factor affecting most of the carbon emissions induced by land cover and land use change in Shaanxi, and socioeconomic factors generally had a greater influence than natural factors. Socioeconomic variables also showed evident spatial heterogeneity in carbon emissions. The results of this study may aid in the formulation of land use policy that is based on reducing carbon emissions in developing areas of China, as well as contribute to transitioning into a "low-carbon" economy.
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Affiliation(s)
- Wei Fang
- School of Water and Environment, Chang'an University, Xi'an, 710054, China
| | - Pingping Luo
- School of Water and Environment, Chang'an University, Xi'an, 710054, China.
- Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Ministry of Education, Chang'an University, Xi'an, 710054, China.
- Xi'an Monitoring, Modelling and Early Warning of Watershed Spatial Hydrology International Science and Technology Cooperation Base, Chang'an University, Xi'an, 710054, China.
| | - Lintao Luo
- Shaanxi Provincial Land Engineering Construction Group, Xi'an, 710075, China
| | - Xianbao Zha
- Disaster Prevention Research Institute, Kyoto University, Kyoto, 611-0011, Japan
| | - Daniel Nover
- School of Engineering, University of California - Merced, 5200 Lake R, Merced, CA, 95343, USA
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Nie Y, Zhou Y, Wang H, Zeng L, Bao W. How does the robot adoption promote carbon reduction?: spatial correlation and heterogeneity analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:113609-113621. [PMID: 37851265 DOI: 10.1007/s11356-023-30424-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 10/08/2023] [Indexed: 10/19/2023]
Abstract
Along with the continuous improvement of industrial intelligence, robots are widely used in various aspects of production and life, playing an essential role in achieving carbon reduction targets. However, the existing research on the carbon reduction effect of robots and its mechanism is limited. Therefore, this study aims to explore the impact of robot adoption on carbon emissions and analyzes the mechanism by taking 30 provinces in China from 2006 to 2019 as research objects. It found that robot adoption can significantly reduce carbon emissions. However, the degree of marketization plays a masking effect, which limits robots' carbon reduction effect to some extent. Furthermore, the carbon reduction effect of robot adoption is stronger in provinces with lower carbon emissions. Finally, robot adoption has a significant spatial spillover effect on neighboring regions. The improvement of robot adoption will positively affect the region's and surrounding areas' carbon emission reduction. The relevant findings provide empirical support for further deepening the policy implementation of robot-assisted carbon emission reduction.
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Affiliation(s)
- Yang Nie
- Guanghua School of Management, Peking University, Beijing, 100871, China
| | - Yang Zhou
- Department of Economics, The Party School of Zhejiang Provincial Committee of the Communist Party of China, Hangzhou, 311121, China
- Zhejiang "Eight Eight Strategy" Innovation and Development Research Institute, Hangzhou, 311121, China
| | - Hankun Wang
- Department of Public & International Affairs, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Kowloon, Hong Kong, 999077, China.
| | - Liangen Zeng
- School of Public Policy and Management, Guangxi University, Nanning, 530004, China
| | - Wenchu Bao
- Guanghua School of Management, Peking University, Beijing, 100871, China
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Yanfeng G, Qinfeng X, Ziwei Y. Matching degree evaluation between new urbanization and carbon emission system in China: a case study of Anhui Province in China. Sci Rep 2023; 13:11724. [PMID: 37474636 PMCID: PMC10359323 DOI: 10.1038/s41598-023-38971-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 07/18/2023] [Indexed: 07/22/2023] Open
Abstract
In order to reveal the relationship between new urbanization and carbon emission to provide reference opinions for the construction of low-carbon urbanization, an evaluation system between new urbanization and carbon emission was constructed. Then their matching degree relationship was analyzed by coupling coordination degree model based on the data from 2012 to 2021 in Anhui Province, and their development trend from 2023 to 2032 was predicted by gray prediction model. The results show that: (1) New urbanization and carbon emission have the co-trend effect, and the consistency of core impact factors is relatively significant. Among them, the level of new urbanization increases from 0.058 in 2012 to 0.699 in 2021 and carbon emission development increases from 0.023 in 2012 to 0.165 in 2021, which both showing an upward trend. Meanwhile, social urbanization and population carbon emission are the core influencing factors. (2) The coupling coordination degree between new urbanization and carbon emission is low, but the synergy trend is optimistic and there is a large room for improvement. Among them, the coupling coordination coefficient of the coupling system rises from 0.136 in 2012 to 1.412 in 2021 (antagonistic phase), and then reaches 0.820 by 2032 (highly coordinated phase) by forecast. It shows that their current development is unbalanced, but the development trend is good, and there is a chance for improvement. This paper deepens the understanding of the logical correlation between new urbanization and carbon emission, and the following views are formed: (1) Low-carbon development is still the mainstream of new urbanization; (2) The coordination development of new urbanization and carbon emission reduction should be strengthened.
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Affiliation(s)
- Gou Yanfeng
- State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
| | - Xing Qinfeng
- State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan, 232001, Anhui, China.
| | - Yang Ziwei
- State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan, 232001, Anhui, China
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Fu H, Li B, Liu X, Zheng J, Yin S, Jiang H. Spatio-Temporal Coupling Evolution of Urbanisation and Carbon Emission in the Yangtze River Economic Belt. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20054483. [PMID: 36901495 PMCID: PMC10002087 DOI: 10.3390/ijerph20054483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 02/27/2023] [Accepted: 03/01/2023] [Indexed: 05/13/2023]
Abstract
The distribution characteristics of urbanisation level and per capita carbon emissions from 2006 to 2019 were investigated by the ranking scale rule, using 108 cities in the Yangtze River Economic Belt of China. A coupling coordination model was established to analyse the relative development relationship between the two, and exploratory spatial-temporal data analysis (ESTDA) was applied to reveal the spatial interaction characteristics and temporal evolution pattern of the coupling coordination degree. The results demonstrate that: (1) The urbanisation level and per capita carbon emissions of the Yangtze River Economic Belt show a stable spatial structure of 'high in the east and low in the west'. (2) The coupling and coordination degree of urbanisation level and carbon emissions show a trend of 'decreasing and then increasing', with a spatial distribution of 'high in the east and low in the west'. (3) The spatial structure exhibits strong stability, dependence, and integration. The stability is enhanced from west to east, the coupling coordination degree has strong transfer inertia, and the spatial pattern's path dependence and locking characteristics show a trend of weak fluctuation. Therefore, the coupling and coordination analysis is required for the coordinated development of urbanisation and carbon emission reduction.
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Affiliation(s)
- Huijuan Fu
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
| | - Bo Li
- School of Management, Tianjin University of Technology, Tianjin 300384, China
| | - Xiuqing Liu
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China
| | - Jiayi Zheng
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China
| | - Shanggang Yin
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China
- Correspondence: (S.Y.); (H.J.); Tel.: +86-10-0579-82282273 (H.J.)
| | - Haining Jiang
- College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China
- Correspondence: (S.Y.); (H.J.); Tel.: +86-10-0579-82282273 (H.J.)
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