How does the digital economy affect the development of the green economy? Evidence from Chinese cities.

The digital economy may accelerate the upgrading of industrial structures and boost regional innovation output, effectively contributing to China's green economic transformation. The impact of the digital economy on developing the urban green economy is analyzed using data from 280 cities across China from 2010-2019. Using a fixed-effects model and the Spatial Durbin model, the digital economy is found to have a significant impact on urban green economy development. This result is shown to be robust to various factors. There is significant regional variability in the impact of the digital economy on green economic growth, with the strongest impact in the northeast, followed by the central and western regions. Meanwhile, non-resource-based cities and policy pilot cities have a more pronounced role in promoting the digital economy. The intermediate transmission chain of industrial structural upgrading and regional innovation output fosters the growth of the urban green economy via the digital economy. Regional innovation production is responsible for 30.848% of this growth, with the intermediate effect of industrial structural upgrading contributing to 38.155%.

COVID-19, Green Deal and recovery plan permanently change emissions and prices in EU ETS Phase IV.

The EU emissions trading system's (ETS) invalidation rule implies that shocks and overlapping policies can change cumulative carbon emissions. This paper explains these mechanisms and simulates the effect of COVID-19, the European Green Deal, and the recovery stimulus package on cumulative EU ETS emissions and allowance prices. Our results indicate that the negative demand shock of the pandemic should have a limited effect on allowance prices and rather translates into lower cumulative carbon emissions. Aligning EU ETS with the 2030 reduction target of -55% might increase allowance prices to 45-94 €/ton CO2 today and reduce cumulative carbon emissions to 14.2-18.3 GtCO2 compared to 23.5-33.1 GtCO2 under a -40% 2030 reduction target. Our results crucially depend on when the waterbed will be sealed again, which is an endogenous market outcome, driven by the EU ETS design, shocks and overlapping climate policies such as the recovery plan.

Financial impact of energy efficiency and energy policies aimed at power sector reforms: mediating role of financing in the power sector.

This research examines how financial transformative power sector reforms affect energy efficiency and the economy in a sample of economies from South Asia, the Middle East, and Europe. We applied two stages of OLS, Bayesian VAR, and Data Envelopment Analysis (DEA) methods to a panel data set from 1995 to 2018. According to empirical findings, institutional deficiency has a negative effect on electricity reforms, implying that the greater the impact of reforms on electricity performance, the higher the institutional efficiency, A collection of reform initiatives involving a variety of reform agencies will boost energy efficiency by up to 13% and per capita electricity access by 62%. Despite recent reforms and regulatory measures, the electricity sector continues to face challenges in terms of private investment and structural flaws such as energy inefficiency, significant technological and financial losses, low power quality, and outdated transmission and network infrastructure. Interestingly 13.2% increases can be found in energy efficiency after electricity reforms. Unlike previous studies, our findings reveal a conflict between the broader economic effects and the welfare impact on electricity consumers.

Impact of Green financing, FinTech, and financial inclusion on energy efficiency.

In modern times, enhancing energy efficiency is one of the core agenda items for the economies to progress the world. United Nations general assembly has recommended increasing energy efficiency by considering the SDG-7 guidelines. In the contemporary period, E7 economies are deficient in producing the financial resources to ensure the availability of funds for the acquisition of energy efficiency. COVID-19 crises, lack of resilience in economies, devastating fiscal burdens, and tight monetary conditions of E7 economies are the major barriers. To resolve such issues, some innovative financing techniques, such as green financing, financial inclusion, and FinTech, were suggested to investigate. However, this research tested the empirical role of financial inclusion, green financing, and FinTech on the energy efficiency of E7 economies. Study findings have shown a significant role of such financing techniques on energy efficiency. Comparatively, green financing is found most fitting and highly supportive financing tool for energy efficiency among the three. The differences in attributes, financing mechanism, funds flow system, transection systems, and variation in support by the financial institution are the main reasons that lessen the role of financial inclusion and FinTech for energy efficiency. However, theorists must revisit the transaction system of FinTech and financial inclusion parameters like green bonds for energy efficiency attainment. Policymakers are suggested to develop viable and energy system-friendly policies to grant green finance to the energy systems of E7 economies, as conveniently as possible.

How financial development affects green energy finance? Relationship between environmental regulation and economic performance.

The construction of green finance index is a three-tiered process that involves macroeconomic, ecological, and monetary indicators. Therefore, this research is one of the first thorough assessments of the impacts of green financing regulations in China, examining 30 provinces during the period of 2010 to 2017. Data envelopment analysis models for 30 provinces in China have been tested by using non-radial models and longitudinal datasets. The findings demonstrate that between 2010 and 2017, the efficiency of China's provinces such as Beijing, Fujian, Guangdong, Hainan, Hebei, Jiangsu, Liaoning, Shandong, Shanghai, Tianjin, and Zhejiang and provinces of central regions as well as western provinces of country green economies has increased with distinct geographical disparities becoming more apparent. The geographical distribution of economic efficacy in the green economy is greatest in the eastern parts and poorest in the Chongqing, Gansu, Guangxi, Guizhou, Inner Mongolia, Ningxia, Qinghai, Shaanxi, Sichuan, Tibet, Xinjiang, and Yunnan. The study revealed that sustainable financial growth may be accomplished via the creation of green financing, which can be achieved by employing different solutions across the macroeconomic, institutional, and ecological considerations. The western and central areas, however, have a significant negative association. There are substantial variations in factors at the state and federal level from the viewpoint of dependent variables. Eventually, the research offers some suggestions for future ecological impact of China, along with the creation of new environmental legislation.

Supply-side options to reduce land requirements of fully renewable electricity in Europe.

Renewable electricity can fully decarbonise the European electricity supply, but large land requirements may cause land-use conflicts. Using a dynamic model that captures renewable fluctuations, I explore the relationship between land requirements and total system cost of different supply-side options in the future. Cost-minimal fully renewable electricity requires some 97,000 km2 (2% of total) land for solar and wind power installations, roughly the size of Portugal, and includes large shares of onshore wind. Replacing onshore wind with offshore wind, utility-scale PV, or rooftop PV reduces land requirements drastically with only small cost penalties. Moving wind power offshore is most cost-effective and reduces land requirements by 50% for a cost penalty of only 5%. Wind power can alternatively be replaced by photovoltaics, leading to a cost penalty of 10% for the same effect. My research shows that fully renewable electricity supply can be designed with very different physical appearances and impacts on landscapes and the population, but at similar cost.

Energy-saving and pricing decisions in a sustainable supply chain considering behavioral concerns.

With the rising environmental concerns among consumers all over the world, sustainability has received considerable attention, and numerous enterprises are adopting various practices such as investing in energy-saving to improve sustainability in supply chains. However, many previous researches always assume that decision makers are perfectly rational and neglect the behavioral concerns of decision makers. This paper considers a two-stage sustainable supply chain with behavioral concerns in order to develop more realistic models, and mainly focuses on the energy-saving and pricing decisions in the decentralized system, as well as how to improve energy-saving level and profits. We develop decentralized decision-making models under two types of behavioral concerns: fairness concern and risk aversion, and derive the optimal strategy for each member with a Stackelberg game in which the manufacturer acts as the leader. The effect of the behavioral concerns on the optimal decisions and corresponding profits is discussed in detail. Theoretical analysis verified by numerical experiments shows that the fairness behavior always causes a negative effect on the manufacturer, total supply chain, and energy conservation, while it could benefit the retailer in profits. The risk aversion behavior always benefits the manufacturer, total supply chain, and energy conservation, whereas it could make the retailer suffer. Note that both the optimal energy-saving level and corresponding profit of the total supply chain under two types of behavioral concerns are lower than that in the centralized system, thereby we propose a revenue-cost-sharing contract to coordinate the supply chain, under which both the manufacturer and the retailer can achieve a win-win outcome and the energy-saving level can be improved. In addition, some managerial implications through our analytical and numerical results are summarized in this paper.

The Energy Consumption of Radiology: Energy- and Cost-saving Opportunities for CT and MRI Operation.

Background Awareness of energy efficiency has been rising in the industrial and residential sectors but only recently in the health care sector. Purpose To measure the energy consumption of modern CT and MRI scanners in a university hospital radiology department and to estimate energy- and cost-saving potential during clinical operation. Materials and Methods Three CT scanners, four MRI scanners, and cooling systems were equipped with kilowatt-hour energy measurement sensors (2-Hz sampling rate). Energy measurements, the scanners' log files, and the radiology information system from the entire year 2015 were analyzed and segmented into scan modes, as follows: net scan (actual imaging), active (room time), idle, and system-on and system-off states (no standby mode was available). Per-examination and peak energy consumption were calculated. Results The aggregated energy consumption imaging 40 276 patients amounted to 614 825 kWh, dedicated cooling systems to 492 624 kWh, representing 44.5% of the combined consumption of 1 107 450 kWh (at a cost of U.S. $199 341). This is equivalent to the usage in a town of 852 people and constituted 4.0% of the total yearly energy consumption at the authors' hospital. Mean consumption per CT examination over 1 year was 1.2 kWh, with a mean energy cost (±standard deviation) of $0.22 ± 0.13. The total energy consumption of one CT scanner for 1 year was 26 226 kWh ($4721 in energy cost). The net consumption per CT examination over 1 year was 3580 kWh, which is comparable to the usage of a two-person household in Switzerland; however, idle state consumption was fourfold that of net consumption (14 289 kWh). Mean MRI consumption over 1 year was 19.9 kWh per examination, with a mean energy cost of $3.57 ± 0.96. The mean consumption for a year in the system-on state was 82 174 kWh per MRI examination and 134 037 kWh for total consumption, for an energy cost of $24 127. Conclusion CT and MRI energy consumption is substantial. Considerable energy- and cost-saving potential is present during nonproductive idle and system-off modes, and this realization could decrease total cost of ownership while increasing energy efficiency. © RSNA, 2020.

Does regional energy consumption disparities assist to control environmental degradation in OBOR: an entropy approach.

China launched the One Belt & One Road (OBOR) initiative to minimize the energy resource shortage. The China's nearby countries are rich in energy resources especially Middle East and North Africa (MENA) and Asian countries which make them ideal locations to cooperate with China in terms of energy resources, as 42.8% of world energy consumption belongs to OBOR countries. The present study elaborates the spatial distribution pattern of energy consumption disparities and its impact on environment. To do this, an entropy approach is utilized to compute the energy consumption inequalities in OBOR and its regions. The spatial and Pareto analysis show that MENA, East, and Southeast Asian economies have the highest degree of energy consumption inequalities, while European and Central Asian economies show the lowest energy consumption inequalities in OBOR region. The long-run estimates indicate that energy consumption inequalities enhance the CO2 emission in OBOR and its region except South and Southeast Asia. Financial development also has a significantly positive impact on CO2 emission in all models for OBOR and its regions except East Asia. Based on findings, the spatial distribution analysis is applicable to maintain balance in regional energy consumption inequality within OBOR and its regions.

New Fixed Assets Investment Project Environmental Performance and Influencing Factors-An Empirical Analysis in China's Optics Valley.

Civilization prospers when the ecology prospers, and civilization decays when the ecology decays. As an effective indicator of sustainable development of economy and resource environment, environmental performance can comprehensively reflect the actual level of coordinated development of the economy and environment system. This paper exemplifies China's Optics Valley to evaluate its environmental performances and research the influencing factors of new fixed assets investment projects, for which a new fixed assets investment project environmental performance assessment system was constructed. The measurement model for the system was constructed using a generalized data envelopment analysis (DEA) with undesirable output to evaluate the environmental performances of the new fixed assets investment projects in China's Optical Valley from 2011 to 2016. The multi-regression model with eight environmental performance hypotheses was established to determine the key influencing factors and to propose targeted countermeasures to enhance low-carbon innovation and promote low-carbon economic development. The results indicated that implementing new fixed assets investment project energy conservation assessments and reviews in high-tech areas could assist companies and the government in achieving better management.

Economic growth and energy consumption in Brazil: cointegration and causality analysis.

Energy is a crucial part of any economy and holds a central position in enhancing social development in the world. Energy consumption and the economy in Brazil have both increased in the past decade. In this paper, time series statistics from 1980 to 2017 will be used to analyze the relationship between real GDP per capita and energy consumption to will examine how energy use in the country affects economic growth using causality models. This is established through testing for stationarity using Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test for trend stationarity. A cointegration relationship is found between the two variables.

Per capita income, trade openness, urbanization, energy consumption, and CO2 emissions: an empirical study on the SAARC Region.

The developing world in general is facing so many crucial problems including global warming in recent years. Global warming has multiple consequences on each segment of the society and therefore, its root causes are important to identify. The present study examines the impact of per capita income, trade openness, urbanization, and energy consumption on CO2 emissions. Countries located in South Asian Association for Regional Cooperation (SAARC) are considered in the study. The selection of the SAARC region is motivated by the diverse nature of its members and further lack of available empirical literature on the same relationship. Annual data from 1980 to 2016 are analyzed using appropriate panel data techniques. The results revealed the presence of environmental Kuznets curve (EKC) in the SAARC region. Further, the introduction of cubic function into the model indicated that the shape of the EKC is N shaped. Besides, trade openness has negative while urbanization and energy consumption have impacted CO2 emissions positively. Moreover, the causality exercise explored a bidirectional causality between urbanization, energy consumption, per capita income, and CO2 emissions. Similarly, energy consumption, per capita GDP, and urbanization are also bidirectionally related. Further, a unidirectional causality running from CO2 emissions, urbanization, and energy consumption to trade openness is detected. Lastly, a unidirectional causality is witnessed from per capita income to energy consumption.

Assessing the solar PV power project site selection in Pakistan: based on AHP-fuzzy VIKOR approach.

Pakistan has an abundant solar power potential which can be effectively utilized for the electricity generation. There are various sites across the country which have sufficient solar irradiation across the year, and thus, suitable for the installation of solar photovoltaic (PV) power projects. This study, therefore, aims to undertake research on the establishment of solar power project site selection in Pakistan. In this context, 14 promising cities of Pakistan are considered as alternatives and studied in terms of economic, environmental, social, location, climate, and orography criteria and further supplemented with 20 sub-criteria. Initially, the analytical hierarchy process (AHP) method has been used to prioritize each of the main criteria and sub-criteria. Later, fuzzy VlseKriterijuska Optimizacija I Komoromisno Resenje (F-VIKOR) method has been employed to prioritize the 14 alternatives. The present investigation reveals that Khuzdar (C2), Badin (C3), and Mastung (C7) are the most suitable cities for the installation of solar PV power projects in Pakistan. Finally, the outcome of the sensitivity analysis revealed that obtained results are reliable and robust for the installation of solar PV power projects in Pakistan. This study shall assist government, energy planners, and policymakers in making cities sustainable by establishing solar power projects in Pakistan.

Impact of affluence and fossil energy on China carbon emissions using STIRPAT model.

Using the extended STIRPAT model, this research examines the influence of various factors on China carbon emission from 1971 to 2014, including total nuclear and alternative energy, total fossil energy, GDP per capita, total population, total urban population, merchandise trade of GDP, and services value added of GDP. Ridge regression was employed to perform the study. The research results show the positivity and significance of all factors on carbon emission. The estimated elastic coefficients reveal the most important factor influencing carbon emission is GDP per capita. Total fossil energy, total urban population, and nuclear energy of total energy use are also prominent influencing factors, while other factors such as value-added services of GDP and merchandise trade of GDP have less significant impacts on carbon emission in China. These findings of the research will be of great significance for China to control its carbon emission in the future and to mitigate global warming to some extent.

Identification and analysis of driving factors of CO2 emissions from economic growth in Pakistan.

This study applied the logarithmic mean Divisia index (LMDI) model to identify and discuss the main drivers of Pakistan's CO2 emissions over the period 1990-2016. The study examined the effects of five factors based on Pakistan's three main economic sectors while considering the 11 types of fuels consumed in that country. The results showed that the energy structure effect is the greatest driving force of CO2 emissions in this country, followed by scale effect and economic structure effect. Energy intensity is the main contributor to reducing Pakistan's carbon emissions throughout the study period. A comparative review at the sectoral level shows that the industrial sector for which coal is the main source of energy supply is the one that contributes the most to CO2 emissions in Pakistan. Alongside this sector is the tertiary sector, where the transport sub-sector imposes rules of conduct based on a growing Pakistani population. Meanwhile, deforestation would be the main cause of CO2 emissions from the agricultural sector in Pakistan, as energy consumption in this sector remains very low. Improving energy efficiency through the intensification of clean energy is urgently needed if Pakistan's environmental goals are to be achieved.