Multi-region comparisons of energy-related CO2 emissions and production water use during energy development in northwestern China

Li, Aijun; Zhou, Dinglin; Chen, Guoshi; Liu, Yu-Hao; Yan, Linbo

doi:10.1016/j.renene.2020.01.060

Cited by 13 publications

(3 citation statements)

References 38 publications

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“…Meanwhile, the western region, with its relatively underdeveloped economy and continued reliance on traditional energy sources, exhibits the least impact from the renewable energy transition. Research by scholars such as Harlan [200], Wang and You [201], Li et al [202], Xu et al [203], and Li et al [204] supports this observation, indicating that the slower integration of renewable energy in the western region leads to less significant reductions in carbon emissions. These regional variations in the effectiveness of energy transition strategies highlight the necessity for customized approaches to environmental sustainability in China.…”

Section: Heterogeneity Test Regarding the Effect Of Energy Transition...mentioning

confidence: 81%

Digitization Meets Energy Transition: Shaping the Future of Environmental Sustainability

Wang,

He,

2024

Energies

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This paper presents an empirical investigation into the effects of energy transition and digitization on carbon dioxide emissions, serving as a proxy for environmental sustainability, across 28 Chinese provinces from 2000 to 2021. Utilizing both static and dynamic regression analyses, the study reveals a significant driving effect of the energy transition on environmental sustainability, primarily through a reduction in carbon dioxide emissions. Notably, this influence is more pronounced in the eastern region of China, followed by the central and western regions, indicating regional heterogeneity in the impact of the energy transition. Furthermore, digitization is found to have a substantial moderating effect, enhancing energy conservation and emission reductions. As digitization progresses, its capability to diminish the impact of energy transitions on carbon dioxide emissions becomes more apparent, particularly in the eastern region, while this effect is less marked in central and western China. The study also delves into the nonlinear relationship between energy transitions and carbon dioxide emissions, discovering that increased levels of digitization can exacerbate the negative effects of energy transitions on emissions. These findings offer valuable insights into the dynamics of energy transition and digitalization, highlighting their crucial roles in shaping environmental sustainability in China.

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Section: Heterogeneity Test Regarding the Effect Of Energy Transition...mentioning

confidence: 81%

Digitization Meets Energy Transition: Shaping the Future of Environmental Sustainability

Wang,

He,

2024

Energies

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“…Cangyuan County has the lowest technology effect index of 0.00, associated with fluctuating declines in water-use efficiency. The results show that improving water-use efficiency is vital to promote the sustainable development of water resources [31]. There is a great need to enhance water-saving and water-recycling technology where there are significant geographical disparities in Lincang City.…”

Section: Influencing Factor Identification Of the Sdg 6 Composite Indexmentioning

confidence: 98%

Sustainable Development Goal 6 Assessment and Attribution Analysis of Underdeveloped Small Regions Using Integrated Multisource Data

et al. 2023

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Data scarcity is a key factor impacting the current emphasis on individual indicators and the distribution of large-scale spatial objects in country-level SDG 6 research. An investigation of progress assessments and factors influencing SDG implementation in cities and counties indicates that smaller-scale regions hold greater operational significance for achieving the 2030 Agenda for Sustainable Development from the bottom up; thus, urgent attention should be given to data deficiencies and inadequate analyses related to SDG impact attribution. This study, conducted in the National Innovative Demonstration Zone for Sustainable Development of Lincang City, investigates multisource data sources such as integrated statistics, survey data, and remote sensing data to analyze the progress and status of SDG 6 achievement from 2015–2020, and employs the LMDI decomposition model to identify influential factors. The assessment results demonstrate that the SDG 6 composite index in Lincang increased from 0.47 to 0.61 between 2015 and 2020. The SDG 6 indicators and SDG 6 composite index have significant spatial heterogeneity. The water resources indexes in wealthy countries are high, the water environment and water ecology indexes in developing countries are comparatively high, and the SDG 6 composite index is high in undeveloped counties. Technological and economic advances are the main positive drivers impacting the SDG 6 composite index, and the relative contributions of technology, economy, structure, and population are 61.84%, 54.16%, −4.03%, and −11.96%, respectively. This study shows that integrated multisource data can compensate for the lack of small-scale regional statistical data when quantitative and comprehensive multi-indicator evaluations of the SDGs are conducted. And, policies related to SDG 6.1.1, SDG 6.2.1, and SDG 6.3.1 can be a priority for implementation in undeveloped regions with limited funding.

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“…The development of industry and manufacturing industries cannot be separated from the supply of energy. With the development of economic globalization, each country cannot stay away from the development of energy [1][2][3]. At present, the world energy market is also suffering from an unprecedented impact with the spread of the economic globalization crisis.…”

Section: Introductionmentioning

confidence: 99%

Risk measurement of China's foreign energy investment portfolio based on Copula-VaR

Liu,

Yang,

Leng

2023

3C TIC

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Energy is an important resource for the development of the country, and investment in energy can promote the development of the national economy. Many scholars are currently using Copula models to predict the risk of energy investments to improve investment efficiency. However, most studies are not systematic enough and focus on countries outside of China. We use the Copula-VaR method with the Archimedean Copula function and the Copula-VaR method with the introduction of tail correlation to calculate the energy futures risk. The risk of six different percentages of China's foreign energy portfolio for three futures on natural gas, oil, and coal between January 3, 2015 and December 30, 2021 is calculated and compared to the traditional method. The results show that the risk values calculated using the improved Copula-VaR model are 0.00836, 0.00922, 0.00217, 0.00635, 0.00612 and 0.00827 higher under the 0.98 confidence level than under the 0.96 confidence level. It has a high accuracy compared with the traditional method. The research in this paper provides an idea for the design of energy investment programs in China

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Multi-region comparisons of energy-related CO2 emissions and production water use during energy development in northwestern China

Cited by 13 publications

References 38 publications

Digitization Meets Energy Transition: Shaping the Future of Environmental Sustainability

Digitization Meets Energy Transition: Shaping the Future of Environmental Sustainability

Sustainable Development Goal 6 Assessment and Attribution Analysis of Underdeveloped Small Regions Using Integrated Multisource Data

Risk measurement of China's foreign energy investment portfolio based on Copula-VaR

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