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Wan R, Qian S, Ruan J, Zhang L, Zhang Z, Zhu S, Jia M, Cai B, Li L, Wu J, Tang L. Modelling monthly-gridded carbon emissions based on nighttime light data. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 354:120391. [PMID: 38364545 DOI: 10.1016/j.jenvman.2024.120391] [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: 11/07/2023] [Revised: 01/25/2024] [Accepted: 02/10/2024] [Indexed: 02/18/2024]
Abstract
Timely and accurate implementation of carbon emissions (CE) analysis and evaluation is necessary for policymaking and management. However, previous inventories, most of which are yearly, provincial or city, and incomplete, have failed to reflect the spatial variations and monthly trends of CE. Based on nighttime light (NTL) data, statistical data, and land use data, in this study, a high-resolution (1 km × 1 km) monthly inventory of CE was developed using back propagation neural network, and the spatiotemporal variations and impact factors of CE at multiple administrative levels was evaluated using spatial autocorrelation model and spatial econometric model. As a large province in terms of both economy and population, Guangdong is facing the severe emission reduction challenges. Therefore, in this study, Guangdong was taken as a case study to explain the method. The results revealed that CE increased unsteadily in Guangdong from 2013 to 2022. Spatially, the high CE areas were distributed in the Pearl River Delta region such as Guangzhou, Shenzhen, and Dongguan, while the low CE areas were distributed in West and East Guangdong. The Global Moran's I decreased from 2013 to 2022 at the city and county levels, suggesting that the inequality of CE in Guangdong steadily decreased at these two administrative levels. Specifically, at the city level, the Global Moran's I gradually decreased from 0.4067 in 2013 to 0.3531 in 2022. In comparison, at the county level, the trend exhibited a slower decline, from 0.3647 in 2013 to 0.3454 in 2022. Furthermore, the analysis of the impact factors revealed that the relationship between CE and gross domestic product was an inverted U-shaped, suggesting the existence of the inverted U-shaped Environmental Kuznets Curve for CE in Guangdong. In addition, the industrial structure had larger positive impact on CE at the different levels. The method developed in this study provides a perspective for establishing high spatiotemporal resolution CE evaluation through NTL data, and the improved inventory of CE could help understand the spatial-temporal variations of CE and formulate regional-monthly-specific emission reduction policies.
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Affiliation(s)
- Ruxing Wan
- School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Shuangyue Qian
- School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Jianhui Ruan
- School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Li Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
| | - Zhe Zhang
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100043, China
| | - Shuying Zhu
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100043, China
| | - Min Jia
- School of Economics and Management, Beihang University, Beijing, 100191, China
| | - Bofeng Cai
- Center for Carbon Neutrality, Chinese Academy of Environmental Planning, Beijing, 100043, China.
| | - Ling Li
- International School of Economics and Management, Capital University of Economics and Business, Beijing, 100070, China
| | - Jun Wu
- School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Ling Tang
- School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China
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Kartal MT, Ulussever T, Pata UK, Depren SK. Time and frequency analysis of daily-based nexus between global CO 2 emissions and electricity generation nexus by novel WLMC approach. Sci Rep 2024; 14:3698. [PMID: 38355707 PMCID: PMC10867028 DOI: 10.1038/s41598-024-54245-z] [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: 07/26/2023] [Accepted: 02/10/2024] [Indexed: 02/16/2024] Open
Abstract
The studies have focused on changes in CO2 emissions over different periods, including the COVID-19 pandemic. Even if CO2 emissions are temporarily reduced during the pandemic according to annual figures, this may be misleading. Considering annual figures is important to understand the overall trend, but using data with much higher frequency (e.g., daily) is much better suited to investigate dynamic relationships and external effects. Therefore, this study comprehensively analyzes the association between CO2 emissions and disaggregated electricity generation (EG) sources across the globe by employing the novel wavelet local multiple correlation (WLMC) approach on daily data from 1st January 2020 to 31st March 2023. The results demonstrate that (1) based on the main statistics, daily CO2 emissions range between 69 MtCO2 and 116 MtCO2, indicating that there is an oscillation, but no sharp changes over the analyzed period. (2) based on the baseline regression using the dynamic ordinary least squares (DOLS) approach, the constructed estimation models have a high predictive ability of CO2 emissions, reaching ~ 94%; (3) in the further analysis employing the WLMC approach, there are significant externalities between EG resources, which affect CO2 emissions. The results present novel insights about time- and frequency-varying effects as well as a disaggregated analysis of the effect of EG on CO2 emissions, demonstrating the significance of the energy transition towards clean sources around the world.
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Affiliation(s)
- Mustafa Tevfik Kartal
- Department of Banking and Finance, European University of Lefke, Lefke, Northern Cyprus, Türkiye.
- Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon.
- Clinic of Economics, Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan.
| | - Talat Ulussever
- Economics and Finance Department, Gulf University for Science and Technology, Mubarak Al-Abdullah, Kuwait
- Center for Sustainable Energy and Economic Development (SEED), Research Fellow, Gulf University for Science and Technology, Mubarak Al-Abdullah, Kuwait
| | - Ugur Korkut Pata
- Department of Banking and Finance, European University of Lefke, Lefke, Northern Cyprus, Türkiye
- Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon
- Clinic of Economics, Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan
- Department of Economics, Osmaniye Korkut Ata University, 80000, Merkez, Osmaniye, Türkiye
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Yu Y, You K, Cai W, Feng W, Li R, Liu Q, Chen L, Liu Y. City-level building operation and end-use carbon emissions dataset from China for 2015-2020. Sci Data 2024; 11:138. [PMID: 38278857 PMCID: PMC10817938 DOI: 10.1038/s41597-024-02971-4] [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: 10/31/2023] [Accepted: 01/15/2024] [Indexed: 01/28/2024] Open
Abstract
The building sector, which accounts for over 20% of China's total energy-related carbon emissions, has great potential to reduce emissions and is critical to achieving China's emissions peak and carbon neutrality targets. However, the lack of data on operational carbon emissions and end-use carbon emissions in the building sector at the city level has become a major barrier to the development of building energy conservation policies and carbon peaking action plans. This study uses a combination of "top-down" and "bottom-up" methods to account for the operational carbon emissions of buildings in 321 cities in China from 2015 to 2020. The energy consumption in buildings is further broken down into six end uses: central heating, distributed heating, cooking and water heating (C&W), lighting, cooling, appliances and others (A&O). The dataset can serve as a reference to support city-level policies on peak building emissions and is of great value for the improvement of the carbon emissions statistical accounting system.
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Affiliation(s)
- Yanhui Yu
- School of Management Science and Real Estate, Chongqing University, Chongqing, 400044, China
| | - Kairui You
- School of Management Science and Real Estate, Chongqing University, Chongqing, 400044, China
- Institute for Carbon Neutrality Technology, Chinese Academy of Sciences - Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China
| | - Weiguang Cai
- School of Management Science and Real Estate, Chongqing University, Chongqing, 400044, China.
| | - Wei Feng
- Institute for Carbon Neutrality Technology, Chinese Academy of Sciences - Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China
| | - Rui Li
- School of Management Science and Real Estate, Chongqing University, Chongqing, 400044, China
| | - Qiqi Liu
- School of Management Science and Real Estate, Chongqing University, Chongqing, 400044, China
| | - Liu Chen
- School of Management Science and Real Estate, Chongqing University, Chongqing, 400044, China
| | - Yuan Liu
- School of Management Science and Real Estate, Chongqing University, Chongqing, 400044, China
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Chen C, Liu W. Advances and future trends in research on carbon emissions reduction in China from the perspective of bibliometrics. PLoS One 2023; 18:e0288661. [PMID: 37471311 PMCID: PMC10358946 DOI: 10.1371/journal.pone.0288661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 06/30/2023] [Indexed: 07/22/2023] Open
Abstract
Addressing global warming is one of the most pressing environmental challenges and a crucial agenda for humanity. In this literature study, we employed bibliometrics to reproduce nearly two decades of research on carbon emission reduction in China, the largest carbon emitter worldwide. The scientometrics analysis was conducted on 1570 academic works published between 2001 and 2021 concerning China's carbon emission reduction to characterize the knowledge landscape. Using CiteSpace and VOSviewer, the basic characteristics, research forces, knowledge base, research topic evolution, and research hotspots were identified and revealed. The analysis results show that the attention to and research on China's carbon emissions have increased in recent years, giving rise to leading institutions and relatively stable core journal groups in this field. The research disciplines are relatively concentrated, but the research collaboration needs strengthening. The research hotspots are mainly carbon emission causes, impacts, and countermeasures in China, and the research frontiers have been constantly advanced and expanded. In the future, research on countermeasures needs more effort, and research cooperation needs to strengthen. The changing landscape of hotspot clusters reveals China's transition towards a low-carbon economy. Through comprehensive analysis of the potential and obstacles to China's transition to low-carbon development, we identified three promising areas of action (low-carbon cities, low-carbon technologies and industries, and transforming China's energy system) and proposed research directions to address remaining gaps systematically.
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Affiliation(s)
- Caiyun Chen
- Party School of Nanjing Municipal Committee of CPC, Nanjing, China
- Key Laboratory of Poyang Lake Environment and Resource Utilization, Ministry of Education, School of Resources and Environmental, Nanchang University, Nanchang, China
| | - Wei Liu
- Key Laboratory of Poyang Lake Environment and Resource Utilization, Ministry of Education, School of Resources and Environmental, Nanchang University, Nanchang, China
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Ke P, Deng Z, Zhu B, Zheng B, Wang Y, Boucher O, Arous SB, Zhou C, Andrew RM, Dou X, Sun T, Song X, Li Z, Yan F, Cui D, Hu Y, Huo D, Chang JP, Engelen R, Davis SJ, Ciais P, Liu Z. Carbon Monitor Europe near-real-time daily CO 2 emissions for 27 EU countries and the United Kingdom. Sci Data 2023; 10:374. [PMID: 37291162 DOI: 10.1038/s41597-023-02284-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 05/31/2023] [Indexed: 06/10/2023] Open
Abstract
With the urgent need to implement the EU countries pledges and to monitor the effectiveness of Green Deal plan, Monitoring Reporting and Verification tools are needed to track how emissions are changing for all the sectors. Current official inventories only provide annual estimates of national CO2 emissions with a lag of 1+ year which do not capture the variations of emissions due to recent shocks including COVID lockdowns and economic rebounds, war in Ukraine. Here we present a near-real-time country-level dataset of daily fossil fuel and cement emissions from January 2019 through December 2021 for 27 EU countries and UK, which called Carbon Monitor Europe. The data are calculated separately for six sectors: power, industry, ground transportation, domestic aviation, international aviation and residential. Daily CO2 emissions are estimated from a large set of activity data compiled from different sources. The goal of this dataset is to improve the timeliness and temporal resolution of emissions for European countries, to inform the public and decision makers about current emissions changes in Europe.
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Affiliation(s)
- Piyu Ke
- Department of Earth System Science, Tsinghua University, Beijing, China
- Department of Mathematics and Statistics, Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK
| | - Zhu Deng
- Department of Earth System Science, Tsinghua University, Beijing, China
- Alibaba Cloud, Hangzhou, China
| | - Biqing Zhu
- Department of Earth System Science, Tsinghua University, Beijing, China
- Laboratoire des Sciences du Climate et de l'Environnement LSCE, Orme de Merisiers, 91191, Gif-sur-Yvette, France
| | - Bo Zheng
- Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Yilong Wang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - Olivier Boucher
- Institute Pierre-Simon Laplace, Sorbonne Université/CNRS, Paris, France
| | | | - Chuanlong Zhou
- Laboratoire des Sciences du Climate et de l'Environnement LSCE, Orme de Merisiers, 91191, Gif-sur-Yvette, France
| | - Robbie M Andrew
- CICERO Center for International Climate Research, Oslo, 0349, Norway
| | - Xinyu Dou
- Department of Earth System Science, Tsinghua University, Beijing, China
| | - Taochun Sun
- Department of Earth System Science, Tsinghua University, Beijing, China
| | - Xuanren Song
- Department of Earth System Science, Tsinghua University, Beijing, China
| | - Zhao Li
- Department of Earth System Science, Tsinghua University, Beijing, China
| | - Feifan Yan
- Key Laboratory of Marine Environment and Ecology, and Frontiers Science Center for Deep Ocean Multispheres and Earth System, Ministry of Education, Ocean University of China, Qingdao, 266100, China
| | - Duo Cui
- Department of Earth System Science, Tsinghua University, Beijing, China
| | - Yifan Hu
- Key Laboratory of Sustainable Forest Ecosystem Management, Northeast Forestry University, Harbin, 150040, China
| | - Da Huo
- Department of Earth System Science, Tsinghua University, Beijing, China
- Department of Civil & Mineral Engineering, University of Toronto, Toronto, ON, M5S 1A4, Canada
| | | | - Richard Engelen
- European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK
| | - Steven J Davis
- Department of Earth System Science, University of California, Irvine, 3232 Croul Hall, Irvine, CA, 92697-3100, USA
| | - Philippe Ciais
- Laboratoire des Sciences du Climate et de l'Environnement LSCE, Orme de Merisiers, 91191, Gif-sur-Yvette, France.
- Climate and Atmosphere Research Center (CARE-C) The Cyprus Institute 20 Konstantinou Kavafi Street, 2121, Nicosia, Cyprus.
| | - Zhu Liu
- Department of Earth System Science, Tsinghua University, Beijing, China.
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