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This paper aims to investigate the relationship between gross domestic product (GDP) by sector and energy consumption by type in 16 emerging countries.

The panel model was utilized taking the period 1980-2010.

The results revealed that GDP by sector and energy consumption by type are cointegrated. Moreover, the Granger causality concluded a bi-directional causal relationship between oil, natural gas and renewable energy consumption and the value of the manufacturing, industrial and services sector. Furthermore, a bi-directional causal relationship was also found between coal consumption and the value of the services sector. Furthermore, a one-way causal relationship was found from oil consumption to the value of the agriculture sector, the value of the agriculture sector to coal consumption, and coal consumption to the value of the manufacturing and the industrial sectors.

This study recommended that these countries should increase their renewable energy consumption to achieve their GDP growth.

This study is different from the previous studies, as it disaggregated the GDP into four sectors, namely, agriculture, manufacturing, industrial and the services sector. In addition, this study will disaggregate energy consumption into oil consumption, gas consumption, coal consumption and electricity consumption.

Reductions in emissions intensity have been expressed in commitments of many countries’ intended nationally determined contribution. Energy structure adjustment is one of the main approaches to reduce carbon emissions. This paper aims to study the causal relationship between carbon emission intensity and energy consumption structure in China based on path analysis.

After data collection, this paper performs correlation analysis, regression and path analysis.

Correlation results display clear collinearity among energy structure variables. Regression finds that coal, oil, natural gas and technology can be used as indicators for carbon intensity while primary electricity has been excluded. Path analysis shows that coal had the largest direct and positive impact on emission intensity. Natural gas had a positive direct and negative indirect effect through its negative relationship with coal on emission intensity. Technology has the largest negative elasticity while all fossil energies are positive. Results indicate a negative effect of energy structure adjustment on China’s national carbon intensity.

Given the major role of China in global climate change mitigation, significant future reductions in China’s CO₂ emissions will require transformation toward low-carbon energy systems. Considering the important role in mitigating global climate change, China needs to transition toward a low-carbon energy system to significantly reduce its carbon intensity in the future.

In the creation of coal consumption reduction policy, the Chinese Government selected eight provinces as key provinces and encouraged them to replace coal with other non-coal energy, involving Kaldor-Hicks improvement. The purpose of this paper is to answer how much coal can be conserved if Kaldor-Hicks improvement is considered.

A DEA model reflecting Kaldor-Hicks improvement is suggested to calculate the potential of coal reduction, and an endogenous directional distance function model is proposed to calculate the energy consumption efficiency.

The results show that the non-key provinces wasted more coal than the key provinces, although the latter are of more concern. From the Kaldor-Hicks criterion, ten non-key provinces in a certain year can save more coal from an energy substitution policy and eight key provinces cannot. Through measurement of coal and energy efficiency, it is also found that provinces with an abundance of coal perform the worst in China.

Previous studies measuring Chinese saving potential of coal mainly focused on Pareto-Koopmans criterion, not consistent to policy practices. This study changes to Kaldor-Hicks criterion, allowing to make some factors better off and other factors worse off.

The growth of the Indian economy is accompanied by the rising trend of energy utilisation and its devastating effect on the environment. It is vital to understand the nexus between energy utilisation, climate and environment degradation and growth to devise a constructive policy framework for achieving the goal of sustainable growth. This study aims to analyse the long- and short-run association and direction of association between energy utilisation, carbon emission and growth of the Indian economy in the presence of structural break.

The study probes the association and direction of association between variables at both aggregate (total energy utilisation, total carbon emission and gross domestic product [GDP]) and disaggregates level (coal utilisation and coal emission, oil utilisation and oil emission, natural gas utilisation and natural gas emission along with GDP) over the time period of 50 years, i.e. 1971–2020. Autoregressive distributed lag model is used to examine the association between the variables and presence of structural break is confirmed with the help of Zivot–Andrews unit root test. To check the direction of association, vector error correction model Granger causality is performed.

Aggregate carbon emissions are affected positively by aggregate energy consumption and GDP in both short and long run. Bidirectional causality exists between total emissions and GDP, whereas a unidirectional causality runs from energy consumption towards carbon emission and GDP in the long run. At disaggregate level, consumption of coal energy impacts positively, whereas GDP influences coal emission negatively in the long run only. Furthermore, consumption of oil and GDP influences oil emissions positively in the long run. Lastly, natural gas is the energy source that has the fewest emissions in both short and long run.

There is a rapidly growing body of research on the connections and cause-and-effect relationships between energy use, economic growth and carbon emissions, but it has not conclusively proved how important the presence of structural breaks or changes within the economy is in shaping the outcomes of the aforementioned variables, especially when focusing on the Indian economy. By including the impact of structural break on the association between energy use, carbon emission and growth, where energy use and carbon emission are evaluated at both aggregate and disaggregate level, the current study aims to fill this gap in Indian literature.

This study aims to examine the relationships between aggregated and disaggregated energy use in the industrial sector, carbon emissions and industrial output in China.

The study utilizes annual frequency data for the period of 1984-2015. The unit root properties of data are tested using augmented Dickey–Fuller and Phillips and Perron unit root tests. Furthermore, the Zivot–Andrew structural breaks unit root test is used to detect the structural breaks steaming into series. The autoregressive distributed lag bound test and newly developed Bayer–Hanck combined cointegration are used to check the existence of a cointegration relationship between underlying variables. Last, the direction of causality is determined applying vector error correction model (VECM) Granger causality.

The results confirm the existence of a long-run relationship in the presence of structural breaks. The authors conclude that aggregated and disaggregated energy consumption in the industrial sector increases CO₂ emission in both long and short run. The VECM Granger causality analysis indicates the bidirectional relationships between CO₂ emission, industrial growth and aggregated and disaggregated (coal, oil and natural gas) energy consumption.

Based on the empirical results mentioned above, the study proposes the recommendation that China should focus on the use of natural gas in the industrial sector instead of coal and oil consumption. The most potent reasons for such a transformation are twofold: natural gas is much more environment-friendly, thus being a much lesser polluting source of energy, and, most significantly, such a change would have no adverse impact upon the output level.

This study contributes to the existing literature on estimating CO₂ emission by using aggregated and disaggregated energy consumption in case of China. Notwithstanding, it also adds to the existing applied literature by using newly developed combined cointegration to confirm and substantiate the cointegration relationship between the underlying variables. Moreover, this study incorporates the role of structural breaks while investigating CO₂ emission function, which helps in providing more valuable policy suggestions.