Analysis of the Relative Price in China’s Energy Market for Reducing the Emissions from Consumption

Promotion policies for renewable energy and their effects in Taiwan

BackgroundThe prices of energy resources are important determinants of sustainable energy development, yet associated with significant unknowns. The estimates of the impact of prices of energy products in the domestic market (for domestic consumers) are rare—hence the importance and novelty of this research. Therefore, the main goal of the paper is to assess the impact of domestic prices of gasoline, gas, coal, and solar energy on sustainable and secure energy future.MethodsThe research includes 14 countries (of which 7 are developed and 7 are developing countries) and a period of 5 years (2014–2018). The model also includes discrete variables: level of development (developing or developed), and the fact as to whether the country is an energy exporter or not. For the purposes of analysis, the following elements were used: Panel Data Analysis, Linear regression (with random and fixed effects), Durbin–Wu–Hausman test, and Honda test, with the use of R-studio software for statistical computing.ResultsThe research showed that the biggest negative impact on energy sustainability was recorded by an increase in the price of coal and the smallest one by an increase in the price of solar energy. An increase in the price of gasoline has a positive impact, while an increase in the price of gas has no impact. The basic methodological result showed that the fixed effects linear model is more accurate than the random effect model.ConclusionsThe results of the paper, important as a sustainable energy policy recommendation, showed that the impact of changes in energy product prices is significantly greater in developing countries, but that the status of the country as an energy exporter has no significance. In addition, the paper points to the need to intensify the research on the assessment of the impact of energy product prices for domestic consumers on their ability to pay that price, because with a certain (so far undefined) increase in energy product prices, a certain group of domestic consumers moves into a category that is not in line with sustainable energy development and is extremely undesirable in every respect—energy poverty.

This study aims to analyze the relationship between carbon dioxide (CO2) emissions, economic growth, and coal and oil consumption in Brazil, Russia, India, China, Turkey, and South Africa by using the bounds test approach autoregressive distributed lag (ARDL) over the period from 1969 to 2011. According to ARDL analysis results, it is determined short-run and long-run relationships among selected variables. Three long-run estimators: ARDL cointegration, dynamic ordinary least squares, and fully modified ordinary least squares are utilized to test the robustness of the estimation results. The Granger causality and the forecast error variance decomposition approaches indicate the evidence of a causal relation between variables. According to empirical results, there are the evidence of a uni-directional Granger causality from real gross domestic product (GDP) to carbon dioxide (CO2) emissions in analyzed countries, uni-directional causality from coal consumption to carbon dioxide (CO2) emissions, uni-directional causality from oil consumption to carbon dioxide (CO2) emissions in China, India, Turkey, and South Africa and bi-directional causality in Brazil and Russia. Meanwhile, there is bidirectional causality from GDP to coal consumption, from coal consumption to oil consumption for Brazil, Russia, China, Turkey, and South Africa. India's causality results reveal a bidirectional causality from GDP to coal consumption, oil consumption, and carbon dioxide (CO2) emissions, and the results of forecast error variance supported the results of the causality test.

The present study examines whether the Race to the Bottom and Revised EKC scenarios presented by Dasgupta and others (2002) are, with regard to the analytical framework of the Environmental Kuznets Curve (EKC), applicable in Asia to representative environmental indices, such as sulphur emissions and carbon emissions. To carry out this study, a generalized method of moments (GMM) estimation was made, using panel data of 19 economies for the period 1950-2009. The main findings of the analysis on the validity of EKC indicate that sulphur emissions follow the expected inverted U-shape pattern, while carbon emissions tend to increase in line with per capita income in the observed range. As for the Race to the Bottom and Revised EKC scenarios, the latter was verified in sulphur emissions, as their EKC trajectories represent a linkage of the later development of the economy with the lower level of emissions while the former one was not present in neither sulphur nor carbon emissions.

From the perspective of external market shocks, this paper proposed fuzzy integrated vector auto regression (FVAR) model that determines the long-term basis and short-term basis interactions of China’s coal price with international energy prices. The proposed FVAR preform coal price fluctuation based on long-term and short term span in six stages including unit root testing, Johansen cointegration test, vector auto regression (VAR) model construction, fuzzification of VAR model, vector error correction (VEC) model and an impulse response function(IRF). It is observed that there is a steady long-term stability and equilibrium bond between the China’s domestic coal price, international coal price and the international crude (unrefined) oil price. The international coal and international crude oil price have an opposite effect on China’s domestic coal price. In addition, the former has a stronger fuzzy price discovery function on China’s domestic coal market than the latter. In the short term, China’s domestic coal price is more complex to instability reactions and is affected by market expectations. The international energy market is more effective than domestic coal market, and there is a relatively stable price adjustment mechanism between the two, with the international coal price playing a leading role in the fuzzy guidance of China’s coal price. Therefore, in reference to international energy pricing models, the paper proposes a fuzzy pricing model for a coal futures index based on the coal futures trading price and supplemented by the premium and discount agreed to by both trading parties.

Has China achieved synergistic reduction of carbon emissions and air pollution? Evidence from 283 Chinese cities

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.

Carbon dioxide emissions and growth of the manufacturing sector: Evidence for China

The mechanism of interaction between economic development, industrial structure and anthropogenic carbon emissions has become one of the focuses of climate change research. In this investigation, Henan Province was studied as an example, wherein the calculation model of carbon emissions in the primary, secondary and tertiary economic sectors was built using the ArcGIS 10.1 software. The spatiotemporal difference of carbon emissions between 2006 and 2015 from the three sectors was studied. The relation between economic development and environmental protection is discussed, based on the construction of a coordination degree model. Conclusions drawn from this analysis are: (1) In 2015, China’s total carbon emissions reached 10,291.93 × 107 t and Henan’s carbon emissions accounted for 1.96% of China’s total carbon emissions. The total carbon emissions in Henan Province increased more than 25.00% between 2006 and 2015. (2) Carbon emissions from different economic sectors demonstrated varied patterns. The primary sector presented a gradual decreasing trend in carbon emission, while the secondary sector showed a fluctuating pattern and the tertiary sector had an inclining trend in carbon emission. (3) There are also disparities in the spatial distribution of carbon emissions from different economic sectors. The primary and tertiary sectors had higher emissions in the southeast and lower emissions in the northwest regions, while the secondary sector showed higher emissions in the northwest and lower emissions in the southeast Between cities at different prefecture levels, differences do not only lie on the quantity of carbon emissions from the three sectors of economy but also a larger variation with regards to the change in quantity of carbon emissions. (4) The coordination degree of economic development was low among different prefecture-level cities. The economic and environmental development appeared coordinated among cities at the same prefecture level; however, coordination degrees among different prefecture-level cities varies significantly.

China's carbon emission trading market has gradually attracted worldwide attention. In this paper, a structural VAR model and Shenzhen, a typical city in China, are selected to study the dynamic relationships between China's carbon emission rights price, energy prices, macroeconomic level, and weather conditions. Shanghai crude oil futures, the first crude oil futures contract in China, is used to describe changes in oil market as a substitute for Daqing crude oil price. The results show that the price of carbon emission rights is mainly affected by its own historical price; and the price of carbon emission rights is positively correlated with crude oil price and natural gas price, but negatively correlated with coal price; the change of macroeconomic level will still have a relatively large impact on carbon emission rights price in the current stage of economic development in China, but this impact is not significant; The impact of weather conditions on the price of carbon emission rights is not obvious. It is found that the launch of the national unified carbon market has indeed achieved certain results, but the situation that China's carbon market is still in its infancy has not been changed; further efforts are needed.

Among studies of the factors that influence carbon emissions and related regulations, economic aggregates, industrial structures, energy structures, population levels, and energy prices have been extensively explored, whereas studies from the perspective of fiscal leverage, particularly of local government investment (LGI), are rare. Of the limited number of studies on the effect of LGI on carbon emissions, most focus on its direct effect. Few studies consider regulatory effects, and there is a lack of emphasis on local areas. Using a cointegration test, a panel data model and clustering analysis based on Chinese data between 2000 and 2013, this study measures the direct role of LGI in carbon dioxide (CO2) emissions reduction. First, overall, within the sample time period, a 1% increase in LGI inhibits carbon emissions by 0.8906% and 0.5851% through its influence on the industrial structure and energy efficiency, respectively, with the industrial structure path playing a greater role than the efficiency path. Second, carbon emissions to some extent exhibit inertia. The previous year’s carbon emissions impact the following year’s carbon emissions by 0.5375%. Thus, if a reduction in carbon emissions in the previous year has a positive effect, then the carbon emissions reduction effect generated by LGI in the following year will be magnified. Third, LGI can effectively reduce carbon emissions, but there are significant regional differences in its impact. For example, in some provinces, such as Sichuan and Anhui, economic growth has not been decoupled from carbon emissions. Fourth, the carbon emissions reduction effect in the 30 provinces and municipalities sampled in this study can be classified into five categories—strong, relatively strong, medium, relatively weak and weak—based on the degree of local governments’ regulation of carbon emissions. The carbon emissions reduction effect of LGI is significant in the western and central regions of China but not in the eastern and northeast regions. This study helps overcome the limitations of previous studies on the regulatory effects of LGI on carbon emissions, and the constructed model may more closely reflect actual economic conditions. Moreover, the current study can benefit countries similar to China that aim to objectively identify the impacts of their LGI on carbon emissions, and such countries can use it as a reference in the formulation of investment policies based on their economic and industrial characteristics.

Effectively addressing today’s energy challenges requires advanced technologies along with policies that influence economic markets while advancing the public good.

Examining the linkage between economic policy uncertainty, coal price, and carbon pricing in China: Evidence from pilot carbon markets

Oil prices have stagnated from 2015 to early 2020. After the crisis caused by Covid-19, oil prices slowly increased and even reached their highest point in the last 10 years in mid-2022. Coal prices reached an all-time high at the end of 2021 before then increasing again in early 2022. The significant increase in coal and oil prices was triggered by the war between Russia and Ukraine. The embargo on Russian oil and coal has contributed to the decline in coal and oil stocks as fuel for power plants in Europe. This research determines the impact of the volatility of oil prices, coal prices, and global energy price index on the stock price index in Indonesia. Does global energy price index, which consists of crude oil and coal prices affect Indonesia stock indices more precisely. This study show volatility of oil prices and coal prices affects company stock prices in Europe. The analysis examined Jakarta Composite Index, LQ45 Index, Jakarta Islamic Index, and Sectoral Index for the 2018-2022 periods. Several techniques, including ordinary least square, linear regression, and multiple regression. The increases of the oil prices, coal prices, and global energy indices affect the stock price index in Indonesia. The study will help investor to understand the impact of oil, coal price volatility and global energy index to several Indonesia stock indices.

The production scheduling problem employing non-identical parallel machines with due dates considering carbon emissions and multiple types of energy sources