Energy Production and Regional Economic Growth in China:  A More Comprehensive Analysis Using a Panel Model

Liu, Yaobin

doi:10.3390/en6031409

Cited by 22 publications

(13 citation statements)

References 19 publications

(21 reference statements)

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“…Another advantage of using panel data is that we are able to allow for cross-sectional dependence. Most of existing studies in this area assumed that all the cross-sections were independent, this could be too restrictive for many cases [29]. In this paper, we will relax the assumption.…”

Section: Literature Reviewmentioning

confidence: 95%

The Relationship between Energy Consumption and Economic Growth: Evidence from China’s Industrial Sectors

Guo

Wang

et al. 2015

Energies

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Abstract:In this article, the relationship between energy consumption and economic growth is examined from the viewpoint of China's industrial sectors. Panel data from 37 industrial sectors in China covering the period from 1998 to 2010 was used in this study. Not only first generation panel unit root tests and panel cointegration tests, but also second generation tests that account for dependence between cross-sectional units were employed. The empirical results reveal that both energy consumption and economic growth are integrated as order one, and they are cointegrated. Panel fully modified ordinary least squares estimators show that a 1% increase in energy consumption increases the real value added of industrial sectors by 0.871%, and a 1% increase in real value added of industrial sectors increases energy consumption by 1.103%. The panel vector error correction models for causality tests are estimated by a system generalized moment method. We find a unidirectional causal relation running from economic growth to energy consumption in the shortrun. In the long run, however, there is evidence of a unidirectional causality running from energy consumption to economic growth. OPEN ACCESSEnergies 2015, 8 9393

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Section: Literature Reviewmentioning

confidence: 95%

The Relationship between Energy Consumption and Economic Growth: Evidence from China’s Industrial Sectors

Guo

Wang

et al. 2015

Energies

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“…The per capita gross domestic product (GDP) variable is the most commonly used for economic analyses when considering only economic development [52], and for impact evaluation studies of oil industry royalties on the economy, such as in the studies by [53,54].…”

Section: Analyzed Development Modelsmentioning

confidence: 99%

Economic and Social Impacts of the Oil Industry on the Brazilian Onshore

et al. 2020

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Oil wealth can foster social inequality if not properly managed. This is due to the migration of production factors from different economic activities to the oil sector. As a result, unemployment, poverty, political instability, a reduction in development, among other undesirable effects on society, may arise. With that in mind, this research evaluates the government’s role in the socioeconomic development of the oil producing municipalities in Rio Grande do Norte State (RN), the largest onshore producer in Brazil. The research uses panel regression as it allows broad analyses of the space and time dimensions to be performed. In addition, it relies on statistical testing at all stages to make the research analysis more appropriate and consistent. The proposed model revealed better socioeconomic development indicators in municipalities where the local Government made public savings as reserve funds. Furthermore, the results show that appropriate public savings management allows municipal governments to design public policies to provide future generations with socioeconomic benefits, in line with the purpose of royalties.

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“…China has become the first country in the world to provide objective, credible information on the state of implementation of circular economy processes, based on a unique system of indicators for political decision-making. The work of Chinese economists [21] critically analyzes the applicative model of the circular Chinese economy. Indicators conceived by the Chinese state can be widely used by other states, also aware of the danger of environmental degradation, as a reference point for knowing the stage of development of the circular economy.…”

Section: Of 16mentioning

confidence: 99%

Modeling the Circular Economy Processes at the EU Level Using an Evaluation Algorithm Based on Shannon Entropy

Buṣu

Bușu

2018

Processes

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In this paper we propose a methodology to study circular economy processes based on mathematical modelling. In open-ended systems, waste could be converted back to recycling, transforming the economy from linear to circular. The concept of entropy and the second law of thermodynamics give the argument for a scale reduction of material circulation. As humans extract more and more energy and matter for the economy, the degree of entropy is likely to increase. Based on the findings of economic studies on the implications of industrialization in the case of growing economies, this study aims at evaluating circular economy processes at the European Union (EU) level using a Shannon-Entropy-based algorithm. An entropy-based analysis was conducted for the 28 European Union countries during the time frame 2007–2016. The modelling process consists of constructing a composite indicator which is composed of a weighted sum of all indicators developed by an algorithm based on Shannon Entropy. The weights assigned to each indicator in our analysis measure the significance of each indicator involved in the development of the composite indicator. The results are similar to the international rakings, consolidating and confirming the accuracy and reliability of this approach.

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Energy Production and Regional Economic Growth in China: A More Comprehensive Analysis Using a Panel Model

Cited by 22 publications

References 19 publications

The Relationship between Energy Consumption and Economic Growth: Evidence from China’s Industrial Sectors

The Relationship between Energy Consumption and Economic Growth: Evidence from China’s Industrial Sectors

Economic and Social Impacts of the Oil Industry on the Brazilian Onshore

Modeling the Circular Economy Processes at the EU Level Using an Evaluation Algorithm Based on Shannon Entropy

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