The economic and environmental impact analysis of replacing fossil energy with electricity in Guangxi—based on Input-Output model

Xu, Yan; Han, Shuai; Cheng, Yuanyuan; Lin, Xiqiao; Qin, Lijuan; Wu, Wanlu; Bo, Zeng

doi:10.1016/j.egypro.2018.09.188

Cited by 3 publications

(4 citation statements)

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“…Using the Johansen Cointegration test and the Granger Causality test, Pandey & Rastogi (2019) examined the interrelationship of electricity consumption and economic growth to environmental degradation in India and found the short-run causality of electricity consumption, economic growth, and CO 2 emissions. Yan et al (2018) proved that China's fast-paced economy relies heavily on electricity supply. In addition, Shengfeng et al (2013) tested the short and long run causal relationship between electricity consumption and real GDP with the VECM method which found the long run relationship between real GDP and electricity consumption, as well as the direct causal relationship between electricity consumption and economic growth both in the short and long run in China.…”

Section: Literature Reviewmentioning

confidence: 99%

- Does electricity consumption influence economic growth in Indonesia?

Sari

2023

JEP

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The purpose of this study was to analyze the influence of electricity consumption, investment, and school enrollment on economic growth of Indonesia from 1984-2018. Electricity is used by society in every activity including production and consumption. This study used ARDL-ECM method. Based on the estimation results, all variables were stationary at the first difference. Based on the empirical findings, the appropriate ARDL model was ARDL (1, 2, 1, 1). In the long run, investment had a positive influence on economic growth and school enrollment had a negative influence on economic growth. Meanwhile, in the long run, electricity consumption is insignificant to the economic performance of Indonesia during the study period. However, in the short run, there was only investment had a significant and positive impact on economic growth in Indonesia. School enrollment and electricity consumption had no significant influence on economic growth. Speed of adjustment (ECT (-1)) had a significant influence on GDP. With the Qusum and QusumQ tests, the model showed feasible stability. Therefore, more vigorous electricity policies should be implemented for supporting economic growth.

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Section: Literature Reviewmentioning

confidence: 99%

- Does electricity consumption influence economic growth in Indonesia?

Sari

2023

JEP

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“…As can be seen from the previous classical models, the recall layer has a simple and efficient recall strategy with a relatively simple model structure and feature combination approach, allowing the recommender system to build recall sets quickly; the ranking layer makes use of all features as much as possible and makes the input features fully crossed through various modules to increase accuracy as much as possible. We have summarised the differences between the two in the following ways [5]. The development of Big Data technology has gone through a process of development from batch processing to stream processing and then to batch-stream integration.…”

Section: Recall Layer Sorting Layer Comparisonmentioning

confidence: 99%

A Study on the Economic Impact of the New Materials Industry on Regional Development Based on Big Data

Wang¹

2023

Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2023, May 19–21, 2023, Han

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In the information explosion mobile Internet era, while people enjoy rich online services, they are also plagued by redundant and inefficient information. By mining information related to users and items, recommendation systems can generate accurate and personalised recommendations for users, which can solve these problems to a certain extent. The development of deep learning techniques in recent years has driven the rapid evolution of recommendation algorithms, while at the same time placing greater demands on the feature data of recommendation systems. In order to meet the needs of recommendation algorithms for massive amounts of features and the real-time processing of data, big data tools are needed to process the data and information. This paper builds a new approach to materials industry research and research content based on big data and deep learning technology to lay a very solid foundation and combination for subsequent research in related fields. A new research breakthrough in a new field.

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“…Most studies have claimed to identify the positive economic and environmental effects of China's energy production transformation using the CGE model [29][30][31]. These studies, which normally focused on identifying energy consumption changes, have however exposed a disagreement in the economic and environmental impacts [32][33][34][35][36]. Existing studies agree that clean transformation of energy production, especially for renewable energy development, would effectively reduce carbon emissions and produce green co-benefits in elevating economic growth and employment [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, previous studies suggested opposite analytical results for economic impacts of clean energy substitution. Some proved that clean energy substitution could increase the net values added from both energy and non-energy sectors, as well as total employment [34,35]; others declared that clean energy substitution would cause damage to China's energy production, trade, and economic activities [19,36]. Much attention needs to be paid to assessing the impacts of future clean energy substitution on energy production, the economy, and the environment in China by merits of the CGE model.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Clean Energy Substitution for Polluting Fossil-Fuels in Terminal Energy Consumption on the Economy and Environment in China

Chen

et al. 2019

Sustainability

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China has initiated various dedicated policies on clean energy substitution for polluting fossil-fuels since the early 2010s to alleviate severe carbon emissions and environmental pollution and accelerate clean energy transformation. Using the autoregressive integrated moving average (ARIMA) regression, we project the potentials of substituting coal and oil with clean energy for different production sectors in China toward the year 2030. Based on the projections, a dynamic multi-sectoral computable general equilibrium model, CHINAGEM, is employed to examine: the impacts of future clean energy substitution on China’s energy production, outputs of non-energy sectors, macro-economy, and CO2 emissions. First, we found that most production sectors are projected to replace polluting fossil-fuels with clean energy in their terminal energy consumption in 2017–2030. Second, clean energy substitution enables producing green co-benefits that would enable improvements in energy production structure, reductions in national CO2 emissions, and better real GDP and employment. Third, technological progress in non-fossil-fuel electricity could further benefit China’s clean and low-carbon energy transformation, accelerating the reduction in CO2 emissions and clean energy substitution. Furthermore, the most beneficiary are energy-intensive and high carbon-emission sectors owing to the drop in coal and oil prices, while the most negatively affected are the downstream sectors of electricity. Through research, various tentative improvement policies are recommended, including financial support, renewable electricity development, clean energy utilization technology, and clean coal technologies.

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The economic and environmental impact analysis of replacing fossil energy with electricity in Guangxi—based on Input-Output model

Cited by 3 publications

References 4 publications

- Does electricity consumption influence economic growth in Indonesia?

- Does electricity consumption influence economic growth in Indonesia?

A Study on the Economic Impact of the New Materials Industry on Regional Development Based on Big Data

Impacts of Clean Energy Substitution for Polluting Fossil-Fuels in Terminal Energy Consumption on the Economy and Environment in China

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