Non-clean Energy Research Articles

Connectedness between artificial intelligence, clean energy, and conventional energy markets: Fresh findings from CQ and WLMC techniques

In line with achieving the objectives of COP27 and SDG7, this paper examines the interdependence of the Artificial Intelligence market, clean energy, and conventional energy markets from 19th December 2017 to 5th May 2023 by using Cross-Quantilogram (CQ) and Wavelet Locale Multiple correlations (WLMC) techniques. Heatmaps of CQ show a bidirectional relationship between the AI market and clean energy at lag one with negative cross-quantile dependence evident throughout most quantiles, especially in normal market conditions. It also indicates a positive relationship between AI return rates and the clean energy market, but only when both datasets are in the same extreme quantiles (10th and 90th). Additionally, WMLC results reveal that time, scale, and investment horizons influence the interaction between AI and clean and non-clean energy industries. A considerable positive association exists between the AI market and traditional energy markets, ranging from 0.6 to 0.8. However, during the pandemic, this dependency turned negative, and it has since been minor, with an uptick in co-movement during Russia – Ukraine conflict. Several policy implications are suggested for the clean energy and conventional energy markets in line with AI.

The effect of household cooking fuel transition on self-rated health: evidence from China

ABSTRACT To reduce the health risks caused by air pollution, China is actively promoting a household clean energy transition. Using Chinese Families Panel Survey (CFPS) data, this paper employs both DID and PSM-DID models to investigate the health effect of the household cooking fuel transition. It also examines the heterogeneous impact of this transition in different population groups and the mechanisms responsible for the heterogeneity. The results indicate that the clean energy transition significantly improves residents’ health, but disadvantaged groups, such as individuals with low-income and high-energy burdens who reside in rural areas, receive fewer health benefits from the transition than others. Most members of these groups cannot easily obtain clean energy or replace non-clean energy at a lower cost. To address these issues in energy-related health inequality, the government needs to improve the affordability, accessibility, and reliability of clean energy and increase health subsidies for relatively vulnerable residents.

The connectedness and structural changes among green and conventional energy markets with CO2 emissions in the United States

As the world's biggest oil producer and second-biggest polluter, the United States has set a strict target to cut net CO2 emissions by 50–52 percent from 2005 levels by 2030 and a net zero emission by 2050 through various strategies, particularly clean energy initiatives. This research attempts to examine the effects of five specific renewable and conventional energy subsectors with carbon emission across five sectors, including power (POW), industry (IND), ground transportation (GT), domestic aviation (DA), and residential (RE). The data illustrate the decline and subsequent increase in CO2 emissions throughout the COVID-19 pandemic, the Russia-Ukrainian conflict, and the USA recession periods from January 1 2021 to September 30 2022. Employing the Vector Autoregressive (VAR) model and a novel quantile connectedness framework, it was found that risk transmission between different markets has been exceptionally high, exceeding 80% both in the COVID-19 outbreak and in the aftermath. The finding suggests that the COVID-19 pandemic shock and the Russian-Ukrainian conflict have intensified the connections between carbon emissions and clean and non-clean energy markets. Policymakers should adjust net zero targets to align with changing dynamics, prioritize wind and geothermal energy, and promote energy independence due to rising oil prices. Leverage lithium for electric vehicles and create specific policies for power, industry, transportation, aviation, and residential sectors to manage CO2 emissions effectively.

Weather impact on ambient air pollution and its association with land use types/activities over 5,572 municipalities in Brazil

Quantify the impact of meteorological changes on air pollution levels is the aim of numerous recent studies. However, there is still a lack of investigations assessing the influence of land use/activities on the relationship between climate and air quality. In this study, we used a two-stage design to estimate the influence of land use types and activities on the association between weather changes and air pollution (PM2.5, NO2, SO2, O3) over 5572 municipalities in Brazil. To calculate the influence of recent weather change on air pollution concentration for each municipality, we used the “weather penalty” concept. This approach considers differences in linear trend coefficients between two generalized additive models. Then, using quantile regression, we estimated the effect of land use types and activities (8 variables related to transportation, energy generation, and land use) on weather-related increases in ambient air pollution. We found that an increase in PM2.5 was associated to recent weather changes in most municipalities (average increase of 0.07μg/m3per year) and a decrease in NO2 in most municipalities (average decrease of 0.0003 ppb per year). O3 and SO2 had more intense increases associated with weather changes in the North region. Our findings suggest the most robust positive associations between weather penalties on PM2.5 and areas with non-clean energy and oil refineries (average increase of 0.006μg/m3per year and 0.04μg/m3per year, respectively). We also found positive associations between Pasture areas, urban areas, and transportation and the weather penalties of this pollutant. In contrast, forest areas were negatively associated with PM2.5 penalties. We also found that oil refineries, urban areas, and transportation significantly positively influenced weather penalties for SO2 and O3. Overall, the study highlights the importance of considering the influence of land use types and activities on weather-related changes in ambient air pollution.

RETRACTED: Elasticity of substitution between clean energy and non-clean energy: Evidence from the Chinese electricity industry

At present, China is in a stage of high-quality economic development. Rising demand for electricity has created a lot of CO2 emissions, which has put great pressure on the low-carbon development of China's power industry. Therefore, China attaches great importance to the potential of various clean power generation to replace thermal power generation. Given this, the study examines the potential for substitution of non-clean energy generation (thermal power generation) and clean energy generation (hydropower, nuclear power generation, and other energy generation) from 1993 to 2022 by using the translog production function and provides a scenario analysis of energy substitution for power generation. Firstly, the k-fold cross-validation method is used for ridge regression estimation in this paper, which avoids the subjective bias caused by the ridge trace diagram method used in most of the previous literatures. Secondly, compared with the previous research on the substitution elasticity of the power sector, this paper subdivides the types of clean power energy when estimating the substitution elasticity, which can better analyze the substitution relationship between thermal power and various clean power. Finally, the estimated substitution elasticity of thermal power and various clean energy sources is greater than 1, which indicates that clean energy generation can effectively replace non-clean energy generation. This provides an effective substitution elasticity parameter for the power sector to study low-carbon development. The scenario analysis show China's power sector can increase the proportion of clean power generation to reduce the carbon emission intensity while ensure power supply, which can help the Chinese government adjust the implementation of policies to promote the early peak of carbon emissions and keep carbon emission at a low level in the power sector.

Can the household clean energy transition ameliorate health inequality? Evidence from China.

China is actively encouraging households to replace traditional solid fuels with clean energy. Based on the Chinese Families Panel Survey (CFPS) data, this paper uses propensity scores matching with the difference-in-differences model to examine the impact of clean energy in the household sector on residents' health status, and whether such an energy transition promotes health equity by favoring relatively disadvantaged social groups. The results show that: (1) The use of cleaner cooking fuels can significantly improve residents' health status; (2) The older adult and women have higher health returns from the clean energy transition, demonstrating that, from the perspective of age and gender, the energy transition contributes to the promotion of health equity; (3) The clean energy transition has a lower or insignificant health impact on residents who cannot easily obtain clean energy or replace non-clean energy at an affordable price. Most of these individuals live in low-income, energy-poor, or rural households. Thus, the energy transition exacerbates health inequalities. This paper suggests that to reduce the cost of using clean energy and help address key issues in health inequality, Chinese government efforts should focus on improving the affordability, accessibility, and reliability of clean energy.

A Flexible Cross-Efficiency Model with Partial Preference for Efficiency Evaluation of Clean Transportation Energy

The rapid development of the transportation industry benefits from the consumption of energy, but the excessive dependence on petroleum fuels makes it a major source of air pollution. In order to achieve green and high-quality development of the transportation industry, many countries are committed to scientifically evaluating the utilization efficiency of clean energy, which has attracted wide attention from the whole society. Significantly, without considering the diversity and complexity of pollutants, indicators used in previous studies were unable to cover all pollutants when establishing the evaluation index system. Meanwhile, as an efficient tool, data envelopment analysis (DEA) is extensively used when it comes to efficiency evaluation. However, the absolute preference of existing benevolent and aggressive cross-efficiency models limits its application scenarios. To address the challenges above, an improved flexible cross-efficiency DEA model is proposed considering both same and different benevolence coefficients of decision-making units (DMUs) on the basis of pointing out the inadequacy of the previous model. The concepts of consensus coefficient and group preference are introduced in the aggregation of cross-efficiency. Besides, based on the theory of undesirable output, the consumption of nonclean energy is taken into account as the input indicator to characterize the degree of pollution. The results show that the obtained cross-efficiency value and efficiency ranking of clean transportation energy change sensitively under various benevolent coefficients. There is an important practical significance to consider the independent preference information of DMUs for the evaluation and ranking of cross-efficiency.

Clean energy consumption, sleep duration, and the association of cancer: findings from the China health and retirement longitudinal study.

Studies of the association between clean energy consumption, sleep duration, and cancer are still extremely limited. We aim to investigate the individual or joint role of clean energy consumption, and sleep duration in cancer onset. We used data from the China Health and Retirement Longitudinal Study. Multivariable locally weighted regression (LOESS) models were first used to assess the individual association of daily sleep time with the risk to develop cancer. Multivariate logistic regression models were conducted on the individual and interaction effects of daily sleep time and cooking fuel. We found that short sleep duration (≤6 hours) and non-clean energy consumption were respectively associated with an increased risk of cancer among older Chinese(p<0.05). We assessed daily sleep time in four quartiles, the adjusted odds ratios (AOR), and 95% confidence intervals (95% CI) for participants in the second (5.0 to <6.5 hours), third (6.5 to <8.0 hours), and fourth quartiles (≥8.0 hours) were 0.88 (95% CI: 0.65-1.20), 0.61 (95% CI: 0.40-0.91), and 0.53 (95% CI: 0.37-0.77), respectively. When we set the cutoff point(6.5 hours), participants who slept more than 6 hours had a 39% lower risk of cancer (AOR: 0.61, 95% CI: 0.46-0.79) compared with others. On the other hand, we conducted that exposure to clean fuel from cooking was negatively associated with cancer incidence (AOR: 0.73, 95% CI: 0.54, 0.97). Furthermore, the combination of longer sleep and cleaner fuels showed the lowest OR for cancer (AOR: 0.39, 95% CI: 0.24, 0.65). Our study showed that sleep duration and clean energy consumption were significantly associated with cancer in elderly Chinese people. In addition, the prevalence of cancer was higher among people who slept less than six hours and used non-clean energy sources. Paying greater attention to the effects of sleep duration and clean energy on the risk of cancer may yield practical implications for cancer prevention.

Prospects of natural gas consumption in Pakistan: based on the LMDI-STIRPAT PLSR framework.

The natural gas (NG) forms the sizeable portion of the primary energy consumption in Pakistan. However, its depleting domestic reserves and increasing demand is challenging to balance the supply-demand in the country. This paper investigates the relationship between NG consumption and driving factors using LMDI-STIRPAT PLSR framework. It is learned that fossil energy structure and per capita gross domestic product (GDP) are most influencing factors on NG consumption, followed by non-clean energy structure, energy intensity, and population. The factors were further modelled to forecast the future values of NG consumption for various scenarios. It is found that NG consumption would be 42.107 MTOE under the high development scenario which would be twice the baseline scenario. It is projected that indigenous NG production will fall from 4 to 2 billion cubic feet/day and demand will increase by 1.5 billion cubic feet/day. Therefore, an optimized strategy is required for a long-term solution to cater this increasing supply-demand.

Does raising the minimum wage matter to firms' energy transition?

Environmental issues are closely related to the economic development of human society, and reducing environmental pollution during production is of great significance for environmental protection. This paper utilizes data from Chinese industrial enterprises (2001–2010), and establishes a fixed effects model to examine whether minimum wage standards are important in reducing non-clean energy consumption by enterprises. The results indicate that an increase in the monthly minimum wage leads to a significant reduction in coal consumption by firms, while the change in the hourly minimum wage do not exert a statistically significant influence on coal consumption by firms. The above empirical results remain robust after conducting a series of endogeneity tests. The mechanism test shows that innovation and exports serve as mediating variables between the minimum wage and coal consumption. Specifically, raising the minimum wage can incentivize enterprises to innovate in production and expand their exports, resulting in a decrease in the consumption of non-clean energy. Finally, through the heterogeneity test, we observe that increases in the minimum wage have varying effects on coal consumption among firms with different numbers of employees and different ages. Based on the research findings of this paper, we propose that the government should comprehensively consider and improve relevant systems, formulate scientifically-based minimum wage standards, take into account both workers' rights and interests as well as enterprises' profits, actively promote energy transformation in enterprises, and ultimately achieve long-term development of the social environment.

Spatio-temporal evolution of total factor structural efficiency in China based on the multiple demands of high-quality economic development

ABSTRACT Optimizing factor allocation structure and improving total factor efficiency is crucial for narrowing regional development disparities and promoting high-quality economic development. Therefore, this paper considers the multiple demands of high-quality economic development and incorporates the dual constraints of input structure optimization and non-decreasing environmental welfare into the research framework of total factor structure efficiency (TFSE). It establishes a progressive research path from total factor efficiencies to TFSE, conducts a dynamic measurement from total factor efficiencies to TFSE, and investigates their spatio-temporal evolution. The main conclusions are: China’s TFSE exhibits a stable fluctuating trend with a decreasing regional distribution from the Southeast to the Northwest under the ‘Hu Huanyong Line’. The total factor efficiencies exhibit a decreasing trend of ‘clean energy – capital – environmental construction – labour - non-clean energy’. Significant spatial correlation effects exist for TFSE and total factor efficiencies in China, with a slight decline over time. TFSE exhibits significant conditional convergence, with a convergence speed of 2.03%. The convergence speeds of total factor efficiencies decrease in the following order: ‘non-clean energy – environmental construction – labour - capital – clean energy’. Moreover, regional TFSE and total factor efficiencies exhibit club convergence with positive spatial spillover effects.

An Empirical Study of the Relationship between Clean Energy Consumption and Economic Growth

To achieve the dual carbon objectives, the Chinese government adopted and implemented a policy of restructuring energy consumption. This paper empirically analyses the relationship between clean energy and economic growth in China by modifying the Cobb-Douglas model. Data on GDP, labour, capital, non-clean energy consumption, and clean energy consumption over the period 1990-2021 are selected to be examined using time series measures. The results show that there is no long-term equilibrium relationship between clean energy and economic growth. Based on the result, this paper suggests that the Chinese government should continuously transform its energy structure to boost its reliance on clean energy so that clean energy can advance the dual carbon goals while fostering economic growth. Finally, the paper gives recommendations on restructuring the energy structure, achieving the dual carbon goal, and promoting sustainable economic growth.

Smart and sustainable scheduling of charging events for electric buses

This paper presents a framework for the efficient management of renewable energies to charge a fleet of electric buses (eBuses). Our framework starts with the prediction of clean energy time windows, i.e., periods of time when the production of clean energy exceeds the demand of the country. Then, the optimization phase schedules charging events to reduce the use of non-clean energy to recharge eBuses while passengers are embarking or disembarking. The proposed framework is capable of overcoming the unstable and chaotic nature of wind power generation to operate the fleet without perturbing the quality of service. Our extensive empirical validation with real instances from Ireland suggests that our solutions can significantly reduce non-clean energy consumed on large data sets.

How does air pollution affect household energy expenditure: A micro-empirical study based on avoidance behavior

China is one of the world's most highly-pollutant countries, and over the last few years has experienced severe and persistent haze pollution. Exploring the effects of air pollution on household energy expenditure will provide a more comprehensive and accurate understanding of the economic effects of environmental pollution. It is an important question but remains unanswered because of an estimation endogeneity. Household non-clean energy consumption would increase air pollution. A major challenge in estimates is how to accurately and cleanly identify the unwatched effect of air pollution given the endogeneity. Using the global satellite monitoring data and unique micro-household survey data, we try to construct an instrumental variable to identify the net effect of air pollution on Chinese household energy expenditure. We find that the increase in air pollution has a significantly positive effect on household energy expenditure. The results remain robust after a series of pivotal checks. Our findings highlight that stay-home-avoidance behaviors as a potential mechanism behind the energy-associated effects of air pollution on household energy expenditure. The well-educated, high-income, and urban households in southern China are more likely to engage in stay-home-avoidance behaviors. These findings can provide some useful policy enlightenments for the government in terms of environmental regulation and promotion of household clean energy.

The effect of technological innovation and clean energy consumption on carbon neutrality in top clean energy-consuming countries: A panel estimation

According to the reports of the “Intergovernmental Panel on Climate Change” (IPCC) 2021, it also is absolutely important to limit global temperature rise to 1.90C-1.50C by 2050, and this is only possible by the involvement of green or clean technologies, negative carbon emissions technologies, change in the attitude of the people towards the environment quality, and suitable energy policies are required for achieving carbon neutrality. Given this framework, the study investigates the empirical impact of technological innovations and clean energy utilize on the ecological footprint and emissions for a panel of clean energy-consuming nations during 1990–2019. The findings indicate that clean energy improves environmental quality by reducing both emissions as well as ecological footprint. On contrary, it is also observed that innovations, population, economic growth and the use of non-clean energy have a substantial positive effect on both the emissions and ecological footprint. Thus, the results offer new insights for policymakers to encourage technological innovations for the advancement of both clean energy and negative emissions technologies. Finally, it shows how carbon neutrality can be achieved by clean energy-consuming countries.

Does environmental regulation spur innovation? Quasi-natural experiment in China

The relationship between stringent environmental regulation and the innovation capability of enterprises has been controversial. Treating the target-based sulfur dioxide emissions control policy implemented for the Two Control Zones after 2000 as a quasi-natural experiment, we exploit the difference-in-difference approach to investigate the causal relationship between stringent environmental regulation and technological innovation using the disaggregated Chinese manufacturing firm-level data between 1998 and 2007. Our estimation results indicate that the target-based environmental policy effectively limits the sulfur dioxide emissions of regulated enterprises and facilitates their technological innovation capability. The conclusion that strengthening environmental regulation has contributed to the technological innovation capability of enterprises is still robust after ruling out the effect of China’s integration into the World Trade Organization. However, enterprises with different characteristics might respond heterogeneously to strict environmental regulation. The estimation results of the difference-in-difference-in-difference confirm that heavily polluting enterprises and those exposed to greater environmental protection pressure have experienced more technological progress. Further analyses demonstrate that enterprises’ technological innovation triggered by strict environmental regulation dramatically reduces their non-clean energy consumption and improves their environmental performance. Our research sheds light on the causal relationship between environmental regulation and technological innovation and provides crucial references for governments within developing economies to design scientific environmental policies aimed at searching for win–win solutions for economic growth and eco-environmental protection.

Energy-saving effect of financial development and its dynamic heterogeneity: Empirical evidence from the dynamic panel quantile model

The energy-saving effect of financial development is directly related to the formulation and implementation of financial policies. Considering the inertial characteristics of energy consumption, this study tested the energy-saving effect of financial development and examined its heterogeneity in terms of low-carbon cleaning and policy change. The results were as follows: First, when energy consumption was at the lower quantile, as consumption increased, the promoting impact of financial development on energy consumption decreased. When energy consumption was at the upper quantile, as consumption increased, the restraining impact of financial development on energy consumption increased. Second, an increase in the quantile level showed that financial development exerted an increasingly stronger influence on promoting clean energy consumption. When non-clean energy consumption was at the upper quantile, financial development exerted an increasingly strong inhibitory effect on non-clean energy consumption. Third, before green credit policy changed, the energy-saving effect of financial development was not widespread and obvious. After green credit policy changed, the restraining impact of financial development on energy consumption increased with the level of consumption. Fourth, after green credit policy changed, compared with the increase of financial development toward promoting clean energy consumption, the inhibitory effect of financial development on non-clean energy consumption significantly improved relative to the second case.

Digital finance and the low-carbon energy transition (LCET) from the perspective of capital-biased technical progress

Digital finance is a new driving force for the low-carbon energy transition (LCET). Using panel data on 283 cities in China, we analyze the impact of digital finance on LCET and decompose LCET into low-carbon energy efficiency transition and low-carbon energy structural transition. Digital finance improves carbon total factor productivity and the proportion of clean energy consumption in total energy consumption, which has a significant positive effect on LCET. This positive effect is attributed to the environmental benefits of capital-biased technical progress. Technological progress strengthens the scale effects and price effects of capital and replaces production factors such as energy, especially nonclean energy, thus promoting energy utilization efficiency and clean energy development. Currently, digital finance is more conducive to promoting the low-carbon energy efficiency transition compared with the low-carbon energy structure transition. Strengthening financial supervision and narrowing the digital usage divide optimizes capital supply and generates financial demands so that digital finance can support LCET. Improving the environmental preference of local governments can enhance the positive effect of digital finance on LCET. Digital finance support for LCET is mainly at the stage of broadening the coverage, with a relatively limited effect of deep cultivation. The application depth of digital finance in the field of energy transition should be further strengthened. This study provides a theoretical reference for the means to achieve effective cohesion between capital demand and financial resource supply in the energy transition stage and provides new enlightenment for the realization of the goals of carbon peaking and carbon neutrality.

Does the green credit policy promote the technological innovation of clean energy enterprises? Empirical evidence from China

The technological innovation of clean energy enterprises is conducive to the transformation of energy structure, and the green credit policy is just a potential opportunity for clean energy enterprises to seek financing convenience. This paper constructs quasi-natural experiments during two different lengths of period (2008–2015 and 2008–2020) based on the 2012 Green Credit Guidelines, selecting a group of A-share listed clean energy enterprises and non-clean energy enterprises as samples. The aim is to examine the impact of green credit policies on the technological innovation of clean energy enterprises. This paper further conducts mechanism tests as well as heterogeneity analysis on the basis of the results. The results show that green credit policies can promote technological innovation for clean energy enterprises, and this effect is reinforced with the accumulation of years following policy implementation. The mechanism test indicates that green credit policies affect the level of technological innovation of clean energy enterprises through credit cost and financing constraints. The heterogeneity analysis demonstrates that the impact of green credit policies is more significant in the non-state-owned enterprises and economically developed regions. The results provide insights for the promotion of green credit policy and the ultimate transformation of China’s energy structure, thus providing reference for developing countries and emerging economies. The formulation of green credit policy should take into account the long-term effect, reduce the burden of enterprises from the perspective of credit cost and financing constraints, and also expand the influence of the policy in state-owned enterprises and underdeveloped areas.

Assessing the effect of the coal-to-gas program on air pollution: evidence from China.

This paper studies the relationship between the coal-to-gas program and air pollution in China and provides micro-evidence of the mechanism from the perspective of households. Using daily air pollution and meteorological data between January 1, 2016, and January 1, 2020, we assessed the effect of the coal-to-gas program on air pollution by introducing the regression discontinuity designs in time (RDiT). We found that the coal-to-gas program significantly improved air quality and brought significant economic benefits. In the short term, the coal-to-gas program can lead to more than 10 units of reduction in SO2, PM2.5, and AQI in the treatment group, while it can lead to more than 50 units in the long term. Using the difference-in-differences approach, we found that the coal-to-gas program has significantly reduced air pollution. Combined with the micro-panel data of the China Health and Retirement Longitudinal Study from 2011 to 2018, we found that the coal-to-gas program changes the household heating energy choices and that the probability of coal-fired heating of households in pilot areas is decreasing. The study suggests that non-clean energy in households should be further replaced to continue improving air quality.