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The emission peak and carbon neutrality targets pose a great challenge to carbon emission reduction in the coal industry, and the coal industry will face an all-around deep adjustment. The forecast of coal price is crucial for reducing carbon emissions in the coal industry in an orderly manner under the premise of ensuring national energy security. The volatility and instability of coal prices are a result of multiple influencing factors, making it very difficult to make accurate predictions of coal price changes. We propose in this paper an innovative hybrid forecasting method (CEEMDAN-GWO-CatBoost) for forecasting coal price indexes by combining machine learning models, feature selections, data decomposition, and model interpretation. By combining high forecasting accuracy with good interpretability, this method fills a gap in the field of coal price forecasting. Initially, we examine the factors that influence coal prices from five angles: Supply, demand, macroeconomic factors, freight costs, and substitutes; and we employ Spearman correlation analysis to reduce the complexity of the attribute set and devise a coal price forecasting index system. Secondly, the CEEMDAN method is used to decompose the raw coal price index data into seven intrinsic modal functions and one residual term in order to weaken the volatility of the data caused by complex factors. Next, the CatBoost model hyperparameters are optimized using the Grey Wolf Optimizer algorithm, while the coal price data is fed into the combined forecasting model. Lastly, the SHAP interpretation method is introduced for studying the important indicators affecting coal prices. The experimental results show that the combined CEEMDAN-GWO-CatBoost forecasting model proposed in this paper has significantly better forecasting performance than other comparative models, and the SHAP method employed in this study identifies the macroeconomic environment, freight costs, and coal import volume as significant factors affecting coal prices. As part of the contribution of this paper, specific recommendations are made to the government regarding the formulation of a regulatory policy for the coal industry in the context of carbon neutrality based on the findings of this research.

The emission peak and carbon neutrality targets pose a great challenge to carbon emission reduction in the coal industry, and the coal industry will face an all-around deep adjustment. The forecast of coal price is crucial for reducing carbon emissions in the coal industry in an orderly manner under the premise of ensuring national energy security. The volatility and instability of coal prices are a result of multiple influencing factors, making it very difficult to make accurate predictions of coal price changes. We propose in this paper an innovative hybrid forecasting method (CEEMDAN-GWO-CatBoost) for forecasting coal price indexes by combining machine learning models, feature selections, data decomposition, and model interpretation. By combining high forecasting accuracy with good interpretability, this method fills a gap in the field of coal price forecasting. Initially, we examine the factors that influence coal prices from five angles: Supply, demand, macroeconomic factors, freight costs, and substitutes; and we employ Spearman correlation analysis to reduce the complexity of the attribute set and devise a coal price forecasting index system. Secondly, the CEEMDAN method is used to decompose the raw coal price index data into seven intrinsic modal functions and one residual term in order to weaken the volatility of the data caused by complex factors. Next, the CatBoost model hyperparameters are optimized using the Grey Wolf Optimizer algorithm, while the coal price data is fed into the combined forecasting model. Lastly, the SHAP interpretation method is introduced for studying the important indicators affecting coal prices. The experimental results show that the combined CEEMDAN-GWO-CatBoost forecasting model proposed in this paper has significantly better forecasting performance than other comparative models, and the SHAP method employed in this study identifies the macroeconomic environment, freight costs, and coal import volume as significant factors affecting coal prices. As part of the contribution of this paper, specific recommendations are made to the government regarding the formulation of a regulatory policy for the coal industry in the context of carbon neutrality based on the findings of this research.

Climate change, energy security, and volatile energy prices have been emerging as eminent threats worldwide. To overcome these concerns, energy efficiency could play a positive role. Hence, this study probes whether energy efficiency curbs CO2 emissions in the US, while controlling for trade, economic growth, and population. We use the Fourier-ADF and Fourier-LM tests to discern the unit-roots. Moreover, to render reliable findings, we rely on the novel Fourier ARDL model. The study divulges that energy efficiency plunges emissions in the long- and short-run. In particular, a 1% upsurge in energy efficiency impedes emissions by about 0.37% and 1.07% during the long- and short-term, respectively. Moreover, population and economic growth escalate emissions whether it is long- or short-run. Next, we document that trade upsurges emissions in the long-run. Also, we perform two types of sensitivity analysis to test whether our key results remain the same across different models/methods. Finally, we suggest escalating energy efficiency through investment and technological advancement. Moreover, import tariffs on renewables should be plunged while there should be relatively high tariffs on non-renewables.

Climate change, energy security, and volatile energy prices have been emerging as eminent threats worldwide. To overcome these concerns, energy efficiency could play a positive role. Hence, this study probes whether energy efficiency curbs CO2 emissions in the US, while controlling for trade, economic growth, and population. We use the Fourier-ADF and Fourier-LM tests to discern the unit-roots. Moreover, to render reliable findings, we rely on the novel Fourier ARDL model. The study divulges that energy efficiency plunges emissions in the long- and short-run. In particular, a 1% upsurge in energy efficiency impedes emissions by about 0.37% and 1.07% during the long- and short-term, respectively. Moreover, population and economic growth escalate emissions whether it is long- or short-run. Next, we document that trade upsurges emissions in the long-run. Also, we perform two types of sensitivity analysis to test whether our key results remain the same across different models/methods. Finally, we suggest escalating energy efficiency through investment and technological advancement. Moreover, import tariffs on renewables should be plunged while there should be relatively high tariffs on non-renewables.

While the research on industrial agglomeration has received more attention, there has been little research on its spatial effect and impact mechanism from the micro perspective. On the basis of the industrial cluster data of 600 counties in the Yellow River basin (YRB) from 2010 to 2020, this article investigates the spatial effect of industrial agglomeration in the YRB from the two aspects of time and space via center of gravity analysis and exploratory spatial data analysis and uses the spatial econometric method to research the drivers of county-level industrial agglomeration. The results reveal the following. During the study period, the industrial center of gravity in the YRB presented a moving tendency from east to west, and 2016 was an important turning point. The county-level industrial distribution in the YRB showed an unbalanced trend, but its unbalanced degree exhibited a weakening trend. In terms of the temporal dimension, the county-level industrial agglomeration degree in the YRB presented a significant downward trend and emerged an evolution process of “diffusion–agglomeration–re-diffusion.” Specifically, it gradually diffused from Shandong and Henan to southern Shanxi and Shaanxi. In the spatial dimension, the county-level industrial agglomeration in the YRB was found to have conspicuous spatial autocorrelation, and the spatial spillover effect was prominent. The number of counties with high–high cluster gradually increased and shifted to the west, while the number of counties with low–low agglomeration exhibited a decreasing trend. The degree of government intervention restrains county-level industrial agglomeration, while the external industry level, regional market demand, and urbanization level promote county-level industrial agglomeration. In terms of river basins, in the upstream, the degree of government intervention and industrial structure adjustment inhibit industrial agglomeration, while the regional market demand and urbanization level promote industrial agglomeration. In the midstream, the degree of government intervention inhibits industrial agglomeration, while the external industry level, regional market demand, and industrial structure adjustment promote industrial agglomeration. In the downstream, the external industry level and industrial structure adjustment restrain industrial agglomeration, while the regional market demand and urbanization level promote industrial agglomeration. The conclusions provide a significant reference for the industrial transformation and high-quality economic development of the YRB.

While the research on industrial agglomeration has received more attention, there has been little research on its spatial effect and impact mechanism from the micro perspective. On the basis of the industrial cluster data of 600 counties in the Yellow River basin (YRB) from 2010 to 2020, this article investigates the spatial effect of industrial agglomeration in the YRB from the two aspects of time and space via center of gravity analysis and exploratory spatial data analysis and uses the spatial econometric method to research the drivers of county-level industrial agglomeration. The results reveal the following. During the study period, the industrial center of gravity in the YRB presented a moving tendency from east to west, and 2016 was an important turning point. The county-level industrial distribution in the YRB showed an unbalanced trend, but its unbalanced degree exhibited a weakening trend. In terms of the temporal dimension, the county-level industrial agglomeration degree in the YRB presented a significant downward trend and emerged an evolution process of “diffusion–agglomeration–re-diffusion.” Specifically, it gradually diffused from Shandong and Henan to southern Shanxi and Shaanxi. In the spatial dimension, the county-level industrial agglomeration in the YRB was found to have conspicuous spatial autocorrelation, and the spatial spillover effect was prominent. The number of counties with high–high cluster gradually increased and shifted to the west, while the number of counties with low–low agglomeration exhibited a decreasing trend. The degree of government intervention restrains county-level industrial agglomeration, while the external industry level, regional market demand, and urbanization level promote county-level industrial agglomeration. In terms of river basins, in the upstream, the degree of government intervention and industrial structure adjustment inhibit industrial agglomeration, while the regional market demand and urbanization level promote industrial agglomeration. In the midstream, the degree of government intervention inhibits industrial agglomeration, while the external industry level, regional market demand, and industrial structure adjustment promote industrial agglomeration. In the downstream, the external industry level and industrial structure adjustment restrain industrial agglomeration, while the regional market demand and urbanization level promote industrial agglomeration. The conclusions provide a significant reference for the industrial transformation and high-quality economic development of the YRB.

Continuous warming climate conditions have triggered numerous extreme weather events, exerting an unprecedented impact on agricultural and food production. Based on the panel data of 3,050 small farmers engaged in maize planting from 2009 to 2018 and collected by the National Rural Fixed Observation Point in China, this study uses the Transcendental Logarithmic Production Function model to estimate the impact of temperature, precipitation, and sunshine hours on maize output. Further, considering climate condition heterogeneity, this study analyzes the development potential of five major maize production areas in China. Results show that temperature and precipitation have a positive impact on maize output and that insufficient sunshine hour is an obstacle to the growth of maize output. Five major maize production areas are affected by climate condition differently, entailing the need for tailored response measures. Additionally, land, labor, and material capital input are key factors affecting maize output. Based on conclusion, we put forward the following suggestions to promote sustainable agricultural production, including strengthening the prediction of temperature, precipitation, and sunshine hours in major maize production areas, optimizing the agricultural production layout and the planting structure based on local endowment, enhancing farmers’ adaptive behavior training toward climate change, developing irrigation and water conservation projects.

Continuous warming climate conditions have triggered numerous extreme weather events, exerting an unprecedented impact on agricultural and food production. Based on the panel data of 3,050 small farmers engaged in maize planting from 2009 to 2018 and collected by the National Rural Fixed Observation Point in China, this study uses the Transcendental Logarithmic Production Function model to estimate the impact of temperature, precipitation, and sunshine hours on maize output. Further, considering climate condition heterogeneity, this study analyzes the development potential of five major maize production areas in China. Results show that temperature and precipitation have a positive impact on maize output and that insufficient sunshine hour is an obstacle to the growth of maize output. Five major maize production areas are affected by climate condition differently, entailing the need for tailored response measures. Additionally, land, labor, and material capital input are key factors affecting maize output. Based on conclusion, we put forward the following suggestions to promote sustainable agricultural production, including strengthening the prediction of temperature, precipitation, and sunshine hours in major maize production areas, optimizing the agricultural production layout and the planting structure based on local endowment, enhancing farmers’ adaptive behavior training toward climate change, developing irrigation and water conservation projects.