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China is the top emitter of greenhouse gases in the world. Pulp and paper industry is one of eight the strictly regulated industries about carbon emission in China. However, estimates of carbon emissions from China's pulp and paper industry have been suffered from remarkable uncertainty. It is summarized that is aroused by the inaccuracy of factors evaluating energy consumption, and the huge gap between the calculation standards for assessing energy consumption in the production process and the actual level recorded in the factories. This paper re-evaluated the carbon emission of China's pulp and paper industry from the perspective of life cycle assessment, and applied more accurate emission factors, taking account of the actual energy consumption of the production process in factories, to estimate the actual carbon emissions in maximum. It is found that the national standards about the energy consumption quota of China's pulp and paper industry is no longer applicable, and the actual energy consumption regulated in the production process is far better than the advanced level given by the national standards. The greenhouse gas emission have been seriously overestimated approximately 12.00%–56.17% by international organizations. • Life Cycle Analysis of the China's pulp and paper Industry is conducted thoroughly. • The pulp flow of China's pulp and paper Industry is analyzed. • Energy regulated in factory is far better than the national standards' required. • Carbon emissions of China's pulp and paper Industry are overestimated. [ABSTRACT FROM AUTHOR]

This paper presents an extension of industry modelling within the REMIND integrated assessment model to industry subsectors and a projection of future industry subsector activity and energy demand for different baseline scenarios for use with the REMIND model. The industry sector is the largest greenhouse-gas-emitting energy demand sector and is considered a mitigation bottleneck. At the same time, industry subsectors are heterogeneous and face distinct emission mitigation challenges. By extending the multi-region, general equilibrium integrated assessment model REMIND to an explicit representation of four industry subsectors (cement, chemicals, steel, and other industry production), along with subsector-specific carbon capture and sequestration (CCS), we are able to investigate industry emission mitigation strategies in the context of the entire energy–economy–climate system, covering mitigation options ranging from reduced demand for industrial goods, fuel switching, and electrification to endogenous energy efficiency increases and carbon capture. We also present the derivation of both activity and final energy demand trajectories for the industry subsectors for use with the REMIND model in baseline scenarios, based on short-term continuation of historic trends and long-term global convergence. The system allows for selective variation of specific subsector activity and final energy demand across scenarios and regions to create consistent scenarios for a wide range of socioeconomic drivers and scenario story lines, like the Shared Socioeconomic Pathways (SSPs). [ABSTRACT FROM AUTHOR]

As an essential energy and chemical base in China, carbon reduction in the Energy "Golden Triangle" (EGT) area is significant. This paper used the logarithmic mean Divisia index (LMDI) method to analyze the drivers of carbon emissions from secondary industry energy consumption (CESEC) in EGT from 2005 to 2019 and then used the GM (1,1) method to simulate carbon emissions in 2030. Meanwhile, the decoupling relationship between carbon emissions and economic development was also analyzed using the two-dimensional decoupling model to test the effectiveness of carbon reduction by the region's government. This paper showed the following: (1) CESEC in the EGT area increased from 1.89×108t to 2.617×108 t; (2) the economic output effect is the main factor influencing carbon emissions in the EGT area, followed by population effect and energy structure effect, while energy intensity effect mitigates carbon emissions; and (3) CESEC will peak at 12.362×108t in 2030, leaving an arduous task on carbon reduction. The two-dimensional decoupling condition between carbon emissions and economic growth in the EGT area is low level-weak decoupling (WD-LE) for 2005–2019. The decoupling condition in Yulin and Ningdong is concentrated in low level-expansion connection (EC-LE) and low level-weak decoupling (WD-LE). Furthermore, Erdos reached high level-expansion negative decoupling (END-HE) condition during 2015–2019. Based on the above findings, a low-carbon development strategy for EGT should consider improving emission reduction technologies for high-carbon energy sources like coal, adjusting the energy consumption structure and seeking government policy support for carbon reduction. [ABSTRACT FROM AUTHOR]

Investigating the effect of renewable energy on the drivers of climate change correctly is significant as it is the basic source of climate change mitigation. In the extant literature, its effect on climate change has been estimated predominantly by regressing aggregate rather than sectoral renewable energy use either on aggregate greenhouse gas emissions or the components of greenhouse gases (GHGs) like carbon dioxide emissions. Against this backdrop, the paper investigates if we should estimate the nexus (i) by the causal effects running from aggregate or sectoral renewable energy use to GHG emissions and (ii) by the causal effects running from renewable energy consumption to aggregate GHG emissions or to its components like carbon dioxide (CO2) emissions. To this end, the paper introduces negative and positive (functional) complementarity between sectoral renewable energy consumptions in reducing or increasing GHG emissions, takes 20 OECD countries from 1990 to 2019, and uses augmented and non-augmented auto-regressive distributed lag approach and vector error correction mechanism. The study finds substantial differences among the results coming out of (i) regressing aggregate and sectoral renewable energy consumption on GHG emissions and (ii) regressing renewable energy consumption on aggregate GHG emissions and on CO2 emissions. The paper suggests regressing sectoral rather than aggregate renewable energy consumption on the components of aggregate GHG emissions like CO2 emissions rather than on aggregate GHG emissions to produce workable, specific, and conclusive policy alternatives. [ABSTRACT FROM AUTHOR]

The maritime Internet of Things (MIoT), a maritime version of the Internet of Things (IoT), is envisioned as a promising solution that can provide ubiquitous connectivity over land and sea. Due to the rapid development of maritime activities and the maritime economy, there is a growing demand for computing-intensive and latency-sensitive maritime applications requiring various energy consumption, communication, and computation resources, posing a significant challenge to MIoT devices due to their limited computational ability and battery capacity. Mobile Edge Computing (MEC), which can handle computation tasks at the network's edge more efficiently and with less latency, is emerging as a paradigm for fulfilling the ever-increasing demands of MIoT applications. However, the exponential increase in the number of MIoT devices has increased the system's energy consumption, resulting in increased greenhouse gas emissions and a negative impact on the environment. As a result, it is vital for MIoT networks to take traditional energy usage minimization into account. The integration of renewable energy-harvesting capabilities into base stations or MIoT devices possesses the potential to reduce grid energy consumption and carbon emissions. However, making an effective decision regarding task offloading and resource allocation is crucial for maximizing the utilization of the system's potential resources and minimizing carbon emissions. In this paper, we first propose a green MEC-enabled maritime IoT network architecture to flexibly provide computing-intensive and latency-sensitive applications for MIoT users. Based on the architecture, we formulate the joint task offloading and resource allocation problem by optimizing the total system execution efficiency (including the total size of completed tasks, task execution latency, and the system's carbon emissions) and then propose a deep-deterministic-policy-gradient-based joint optimization strategy to solve the problem, eventually obtaining an effective resolution through continuous action space learning in the changing environment. Finally, simulation results confirm that our proposal can yield good performance in system execution efficiency compared to other benchmarks; that is, it can significantly reduce the system's carbon emissions and tasks' delay and improve the total size of completed tasks. [ABSTRACT FROM AUTHOR]

Considering the substantial environmental and economic consequences, countries worldwide have placed renewable energy at the forefront of their priorities. While previous empirical research has extensively explored the link between renewable energy and economic growth, this study seeks to address an overlooked aspect by investigating the potential impact of a particular indicator on economic activity. This paper examines the relationship between current account balance and renewable energy in OECD countries using an autoregressive distributive lags (ARDL) model to explore symmetric relationships and the non-linear autoregressive distributive lag model (NARDL) approach and panel vector autoregression (P-VAR) model over the period 1995–2020. The results indicate that there is a cointegrating relationship and that renewable energy consumption has a positive impact on the current account balance in the long term, but no significant impact in the short term. Given that many OECD countries are struggling with deficits, prioritizing bioenergy trade would be beneficial to increase the current account surplus. [ABSTRACT FROM AUTHOR]

Climate change is progressing faster than previously envisioned. Efforts to mitigate the challenges of greenhouse gas emissions by countries through the establishment of the Intergovernmental Panel on Climate Change has resulted in continuous environmental improvements in the energy efficiency and carbon emission signatures of products. In this paper, an energy–carbon emissions nexus causal model was applied using the Leontief Input–Output mathematical model for low-carbon products in future transport-manufacturing industries., The relationship between energy savings, energy efficiency, and the carbon intensity of products for the carbon emissions signature of the future transport manufacturing in South Africa was established. The interrelationship between the variables resulted in a 29% improvement in the total energy intensity of the vehicle body part products, 7.22% in the cumulative energy savings, and 16.25% in the energy efficiency. The scope that has been examined in this paper will be interesting to agencies of government, researchers, policymakers, business owners, and practicing engineers in future transport manufacturing and could serve as a fundamental guideline for future studies in these areas. [ABSTRACT FROM AUTHOR]

The phenomenal increase in global temperature and variation in climate change are the replications of nature, alarming governments to limit the emissions of greenhouse gases (GHGs) and adopt green innovation and environmental-friendly clean and green technologies. In this paper, we empirically investigate whether there are any changes in excessive consumption of energy from conventional sources, expansion of urbanization, carbon dioxide (CO2) emissions, and economic growth in six different regions, namely, East Asia (EA), South Asia (SA), Southeast Asia (SEA), Central Asia (CA), Eastern Europe (EE), and the Middle East and North Africa (MENA), under the Belt and Road Initiatives of panel data over the period of 1985 to 2017. The empirical methods include a panel co-integration check, heterogeneity test, panel Granger causality test, pooled mean group (PMG), and augmented mean group (AMG). To verify the outcomes, robustness tests were carried out using the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) approaches. Our results confirm that CO2 emissions are primarily influenced by excessive utilization of conventional energy, economic growth, and expansion of urbanization. The findings confirm the co-integrating relationships among the variables in all six regions. Moreover, the panel causality analysis identified a bidirectional causal relationship between energy consumption, economic growth, urbanization, and CO2 emissions. While these results can play an instrumental role in formulating CO2 emission policies among our selected countries, our research can also assist policymakers and governments in other developing countries implement important policy initiatives. In this regard, the findings suggest that the current environment-related polices of Belt and Road Initiatives (BRI) do not efficiently tackle CO2 emissions. In order to achieve the CO2 emission degradation objective, the Belt and Road countries should restructure their environment-related policies by limiting the consumption of conventional energy and expansion of urbanization. The adaptation and establishment of such a panoramic policy program can assist emerging economies to acquire consolidated and environmentally sustainable economic growth. [ABSTRACT FROM AUTHOR]

As a vital resource endowment area and ecological barrier area in China, low‐carbon development in the upper reaches of the Yangtze River is of great significance. This paper compiled the emission inventory in the Upper Yangtze River Economic Zone in the IPCC regional emission accounting method framework and used the Environmental Kuznets Curve (EKC) model to evaluate economic development and carbon emissions. Finally, this paper used the time series method to predict carbon emissions to 2030. We find that carbon dioxide (CO2) emissions increased rapidly from 2004 to 2012. The average annual growth rate reached 9.16%. After 2012, carbon emissions in the region no longer grow steadily but fluctuate or even decline. The secondary industry contributed 79.77%, the tertiary industry contributed 17.78%, and the primary industry only 2.45% from 2000 to 2015. From the industrial sector and energy consumption, thermoelectricity production and supply division and nonmetallic mineral products industry are the sectors with the most CO2 emissions, accounting for 35.54% and 13.34%, respectively. Raw coal and coke are the main factors causing emissions. Nearly 61.32% of the carbon dioxide produced by raw coal comes from the electricity production sector. However, from 2011 to 2015, the CO2 emissions of raw coal decreased year by year, down by 23.32%. The impact of economic growth on carbon dioxide emissions supports the EKC hypothesis. The CO2 emissions in the Upper Yangtze River Economic Zone will decline after 2020, but Chongqing has shown an upward trend. With the above results, provinces and cities can optimize the regional industrial structure based on sectoral carbon emissions. The study area needs to develop clean energy to optimize the coal‐led energy consumption structure. Provinces and cities in the district can learn from each other's advanced emission reduction experience. © 2021 The Authors. Greenhouse Gases: Science and Technology published by Society of Chemical Industry and John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

The primary aspiration of this paper is to learn about the effects of economic growth (GDPG) and energy consumption (ENRC) on environmental pollution (EP) in G-3 countries and to show the significance of renewable energy consumption (RENEW) on environmental pollution (EP). The data covers the period from 1970 to 2020 by applying the "Pooled Mean Group-Autoregressive Distributed Lag" (PMG-ARDL) model. The results indicate that GDPG is negatively co-integrated with CO2 emissions (pollution) in the short run (SR) but positively co-integrated in the long run (LR). Energy consumption has a positive impact in the long run, but there is no positive impact in the short run to accelerate pollution. In both the short and long run, renewable energy has a significant role in reducing environmental degradation. However, according to the Dumitrescu Hurlin panel, there was bidirectional causality (BC) involving energy consumption and pollution. Because of the large volume of energy emphasized in economic growth and development activities, energy use increases pollution. In addition, there was a BC involving energy consumption and economic growth. At the country level, a significant contribution implies sustainable development and the implication of environmental quality assurance policies. [ABSTRACT FROM AUTHOR]

Effectively utilizing low-grade thermal energy is a promising approach to mitigating greenhouse gas emissions while reducing the burden on centralized power grids. Current thermoacoustic heat pumps and power generators face challenges such as high onset temperature differentials and low performance. This paper addresses these challenges by introducing a gas-liquid resonator into a thermoacoustic combined heat and power systems to recover low-grade thermal energy in residential buildings. Through Sage modeling and calculations, the internal characteristics of the proposed system and its output performance under different operating conditions are explored. At a heating temperature of 350 °C, the system can generate 6.4 kW of output thermal power, 0.9 kW of electricity, and overall exergy efficiency is 79.3 %. Combining neural network models with case studies conducted in Spain and Finland, the system can annually save 5.6 MWh and 20.7 MWh in fuel energy, reduce emissions of 1374 kg and 5180 kg of carbon dioxide, and save a total cost of €611 and €2324, respectively. Furthermore, comparisons with other emerging micro-CHP systems highlight the efficiency of the proposed system. These results indicate the high potential of thermoacoustic combined heat and power systems in recovering low-grade thermal energy and achieving energy savings and emission reductions. • Introducing an innovative thermoacoustic micro-CHP system. • Achieving an exergy efficiency of 79.3 % under standard operating conditions. • One system can annually reduce household CO 2 emissions by 5180 kg. • Ideal for households, balancing lower output levels for both heat and electricity. [ABSTRACT FROM AUTHOR]

Heavy-Duty Vehicles (HDVs) pose a significant challenge in meeting emissions reduction targets. However, studies on estimating emissions from Irish HDV fleet are rare. This paper estimates fuel consumption and CO 2 emissions from tractor-trailers that occupy nearly half of Ireland's HDV fleet using the European Commission recommended VECTO tool. To improve the estimation accuracy, driving cycles specific to Irish road conditions were developed utilising real naturalistic driving data from trucks. The Irish driving cycles significantly differ from the VECTO inbuilt driving cycles in percentage time spent in different gears, fuel consumption, and CO 2 emissions. CO 2 emissions estimated with Irish driving cycles range from 733 to 1961 g/km, while with VECTO driving cycles, the values ranged from 728 to 1865 g/km. Further, the study evaluated the sensitivity towards payload, road gradient, and idling percentages. The study insights would provide an essential evidence base for policymakers in Ireland. [Display omitted] • Developed Irish urban, regional, and freeway driving cycles. • Employed the VECTO tool to assess CO 2 emissions from Irish heavy-duty vehicle fleet. • Focused on tractor-trailers with a major share in the Irish fleet. • Evaluated the effect of idling, gradient, and payload on CO 2 emissions. • Fuel consumption is underestimated by up to 7.1% when the road gradient is ignored. [ABSTRACT FROM AUTHOR]

Recognizing the factors responsible for the gradual increase in greenhouse gas [e.g. carbon dioxide (CO2)] emissions is crucial to reduce the detrimental consequences on environmental sustainability and human life. Accordingly, spotting the sectors which contribute the most to CO2 emissions and dampen economic growth have become one of the major concerns for policymakers around the globe. Against this background, this paper examines the nexus between economic growth and CO2 emissions in three Baltic countries namely Estonia, Latvia and Lithuania. Thus, the study basically checks the validity of the environmental Kuznets curve (EKC) hypothesis by taking into account the role of energy consumption and financial development over the period of 1990–2018. This type of study is highly important for the region in order to comply with the commitments of the Paris Agreement and Sustainable Development Goals of the United Nations. The study first employs appropriate testing procedures and second-generation panel data methods to account for cross-sectional dependency and slope heterogeneity among countries. Applying unit roots and cointegration tests, the study then employed different mean group estimation models and heterogeneous panel causality methods suitable for cross-sectionally dependent and heterogeneous panels. The results of the econometric analyses reveal that the inverted U-shaped EKC hypothesis does not hold in the Baltic countries. But the pollution haven hypothesis is evidenced to hold for these nations. By boosting the CO2 emissions figures, again, the study also revealed that higher levels of energy consumption exhibit adverse environmental consequences. Financial development is found to be effective in explaining the variations in the CO2 emission figures of the selected countries as well. Causality test results confirm bi-directional causality between economic growth and CO2 emissions, energy use and CO2 emissions, CO2 emissions and financial development, energy use and economic growth as well as between energy use and financial development. Furthermore, country-specific impacts are found to be similar to the corresponding panel estimates. Consistent with the findings, the study finally puts forward some policy-level suggestions. Accordingly, it is recommended that the Baltic countries need to move away from fossil-fuel dependent energy consumption growth policies to mitigate environmental degradation. [ABSTRACT FROM AUTHOR]

Previous studies have done more research on the relationship between carbon emission reduction, energy consumption, and economic growth in specific countries or regions, which rarely consider the issue of heterogeneity between countries or regions, and also lack the refinement of energy consumption categories. Using panel data from 2000 to 2017, this paper divided the top 28 global carbon emission countries into developed countries and developing countries, and explores co-integration and causality between renewable energy consumption, non-renewable energy consumption, economic growth, and carbon emission. Results suggested that there is a two-way causal relationship between carbon emissions and economic growth in all economies. There is a two-way causal relationship between economic growth in developed countries and consumption of renewable and non-renewable energy, while there is no significant relationship between economic growth and energy consumption in developing countries. There is a two-way causal relationship between carbon emissions and renewable energy in all economies, but there are significant differences; there is a two-way causal relationship between carbon emissions in developed countries and non-renewable energy, and only one-way causality exists in developing countries. [ABSTRACT FROM AUTHOR]

Household energy consumption has been one of the most critical factors to climate change. We conducted bibliometric research on household energy consumption by using CiteSpace and HistCite. The Web of Science was used to identify and visualize the evolution of paper citation diagram, the collaboration network, the cocitation network and emerging trends by mapping the knowledge domain of 2984 academic publications between 2011 and 2018 related to household energy consumption. The results showed that the number of academic articles published in the field of household energy consumption and carbon emissions was on the rise, but the number of highly cited articles decreased. The most productive authors and institutions in this subject are in Australia, the USA, China, Britain and Germany. Much of the research has focused on electricity consumption and heat supply, while there have been few studies on other subdomains in the area of energy consumption. The impact of greenhouse gases produced by energy consumption on the environment was the focus of this study. Here, we present an in-depth analysis of household energy consumption and carbon emission research to better understand global trends and hot topics in this field which have emerged over the last few years. [ABSTRACT FROM AUTHOR]

This empirical study analyzes the relationship between green energy investments on environmental quality in Organisation for Economic Co‐operation and Development (OECD) countries from 2000 to 2014. Environmental quality was disaggregated into five widely known proxies: carbon emissions, greenhouse gases, carbon monoxide, sulfur oxides, and nitrogen oxides. On one hand, using the system‐Generalized Method of Moments estimator, the results of this paper demonstrate that renewable energy investments, primary energy supply, and economic growth produces negative and significant relationships with the growth of carbon emissions, greenhouse gases, carbon monoxide, sulfur oxides, and nitrogen oxides. On the other hand, the interaction term between renewable energy and primary energy supply shows positive and significant associations with all environmental quality indicators. Furthermore, the present study finds a U‐shaped relationship between environmental quality and income for carbon emissions, greenhouse gases, carbon monoxide, sulfur oxides, and nitrogen oxides, which did not completely confirm existence of the environmental Kuznets curve. Therefore, the overall findings of the paper suggest that renewable energy adoption is of prime importance in lowering the growth in the emission of harmful gases and combatting climate change, although energy patterns and processes in OECD countries must be revised to ensure that they are environmentally friendly. [ABSTRACT FROM AUTHOR]

In today's world, where economic development and environmental sustainability are becoming increasingly important aspects of national strategy, attention to the impact of different economic sectors on climate change is becoming an integral part of scientific research. This article focuses on analyzing the impact of primary and secondary economic sectors development on carbon dioxide (CO 2) emissions at the sub-national level in Russia from 2005 to 2019. The aim of the study is to provide an in-depth understanding of the relationships between the dynamics of these sectors and CO 2 emission levels in different regions of the country. Weighted regression and panel data methods were applied to better identify the patterns of the impact. The results show that the size of population and electricity consumption have the highest impact on CO 2 emissions. So that, the expansion of nuclear and gas generation capacity, as well as significant improvement of energy efficiency, are of crucial importance to reduce the emissions. Other sectors have a heterogeneous impact and requires more differential approaches, considering the specifics of regions. Taking into account the significant differences between the Russian constituent entities, this paper emphasizes the low informativeness of assessments at the national level and their inadequacy in terms of improving the efficiency of domestic management, including decarbonization policies. • The impact of considered economic sectors on CO 2 emissions were analyzed in 80 Russian constituent entities in 2005–2019. • Weighted regression and panel data methods were used to catch the specifics of Federal districts and constituent entities. • Population size and electricity consumption have the highest impact on CO 2 emissions. • Increasing energy efficiency and modernization of energy facilities are the most promising solutions to reduce the emissions. [ABSTRACT FROM AUTHOR]

It is of great significance to establish a carbon emission management system and carbon emission reduction target to put forward emission reduction measures for each subunit of a sewage treatment plant. In this paper, a mathematical model was constructed for calculating carbon emission in the whole sewage treatment system process. Meanwhile, the model calculated the carbon emission changes after upgrading three sewage treatment plants and identified the critical controlling unit. The results showed that the CO2 produced from electric energy consumption and chemical application was the primary source of carbon emission of wastewater treatment. Raising sewage discharge standards appropriately could effectively reduce the carbon emission generated by each link of the wastewater treatment plant. Further improvement of effluent standards could adversely affect sewage treatment plants in terms of energy, resources, and greenhouse gas emissions. In addition, raising the standard of total phosphorus concentration in the effluent may lead to a corresponding increase in the amount of phosphorus removal agents, as well as an increase in indirect carbon emission, material consumption, and chemical sludge. Therefore, it is necessary to develop sewage treatment technologies that are economical, applicable, energy-saving, and environmental friendly to realize the environmental benefits of carbon emission reduction in sewage treatment and sustainable utilization of energy and resource from wastewater. [ABSTRACT FROM AUTHOR]

The foremost theme of the paper is to explore the asymmetric/symmetric impact of energy consumption on the carbon dioxide emission of G7 countries (Germany, Canada, USA, Italy, France, Italy, UK, and Japan). The nonlinear autoregressive distributed lag is used to measure asymmetric/symmetric cointegration by using annual data of G7 countries from 1965 to 2019. The augmented Dickey-Fuller and structural break unit root test is employed to measure the stationarity in variables while the Brock, Dechert, and Scheinkman test is used for measuring nonlinearity and the Wald test is used to figure out short- and long-run asymmetries/symmetries, respectively. The estimated findings of the nonlinear autoregressive distribution lag model show a significant effect of energy use on the ecological footprint. The asymmetric causality test provides evidence of unidirectional, bidirectional, and asymmetrical/symmetrical causality among the variables of G7 nations. The finding of the study suggested policy for the government of Canada and France to use coal instead of oil and gas while the USA, Germany, Italy, UK, and Japan are required to consume gas as compared to oil and coal. Similarly, the study also suggests using modern technology, renewable energy, and preventive measurement for ensuring environmental betterment. [ABSTRACT FROM AUTHOR]

In the global context of intensive energy resource consumption and substantial greenhouse gas emissions, as the energy sector with the highest share of carbon emissions, the power generation industry is destined to play a central role in China's efforts to promote energy conservation and emission reduction. With the emergence of local carbon pilot projects and the implementation of a national carbon market, key emission units in the power industry, in addition to the electricity market business, also need to fulfill and pay carbon emission rights on schedule. The power generation rights are a market-based mechanism for clean energy units to replace conventional energy units in electricity generation, which can promote carbon emission reduction by incorporating the actual carbon emissions from conventional energy units. This paper establishes an optimized social welfare model that incorporates the interdependency between the thermal power generation unit market and the electric carbon market. It is based on the social welfare model of power generation rights and the initial allocation of carbon emission rights for the units, while considering the cost increase of excess emission under monthly forecasted power generation by the thermal power units. Taking a certain hydropower province as the simulation object, it was verified that the optimized model has a higher transaction rate of power generation rights, greater social welfare, and no reduction in unit revenue. • A groundbreaking linkage between power generation rights trading and carbon emissions trading is introduced. • A novel market optimization objective function is presented to enhance social welfare and encourage participation. • An innovative matching method is proposed to boost the transaction rate in power generation rights trading. • The proposed model is corroborated through a real case study in southwestern China. [ABSTRACT FROM AUTHOR]

Given the great importance of achieving sustainable economic growth, however, it has been suggested that the replacement for fossil fuels by renewable energy sources can reduce environmental pollution. Renewable energy can decrease emissions caused by economic activities through increasing renewable energy consumption in energy-intensive-polluted sectors, and adopting environmental-friendly technologies in production. Therefore, this paper examines the linkages between energy-related CO2 emissions, economic growth, and renewable energy consumption for the 48 U.S. states over the period 1997–2017 by employing the panel fixed-effects, the panel-corrected standard errors, the two-step Generalized Method of Moments, and the Method of Moments Quantile Regression with fixed effects developed by Machado and Silva (2019). The results provide strong evidence of an inverted U-shaped relationship be-tween economic growth and environmental degradation, which known as the Environmental Kuznets Curve. Furthermore, this paper confirms that renewable energy consumption, electricity prices, and primary energy prices have negative impact on emissions whereas Heating Degree Days have a positive impact on emissions. Moreover, the panel quantile regression models confirm that the effects of all explanatory variables on CO2 emissions are heterogeneous at different quantiles. The findings of this paper provide evidence of the environmental benefits of promoting in renewable energy and suggest policy tools to reduce emissions through energy price mechanisms. [ABSTRACT FROM AUTHOR]

There is a growing attention on the role of rapidly growing developing countries in mitigating climate change, especially in curbing the emissions of greenhouse gases. Estimates show that in the year 2011, carbon dioxide (CO2) emissions from developing countries constituted about 63% of the world's total, compared with only 37% for developed countries. Thus, developing countries must be an integral part of global actions toward combating climate change. In Saudi Arabia, the energy use per capita of 6937.23 kg of oil equivalent in 2014 was 3.6 times the global average and the per capita CO2 emission of 19.53 metric tons was the seventh highest. With sensitive ecosystems, limited freshwater resources and substantial coastal developments, the country is vulnerable to climate change. As such, the country has recently initiated some sustainable urban planning strategies in its major urban centers as part of its climate change mitigation and adaptation efforts. However, few studies have assessed the extent of the implementation of the strategies. Based on secondary data analysis, this article found that the implementation of the strategies is at infancy with urban greening, public transportation, and green building projects gradually gaining prominence. The paper recommends more focus on rising building and population density, mandating mixed land uses, as well as public enlightenment and engagement about climate change impacts and energy choices. [ABSTRACT FROM AUTHOR]

Governments around the world have enacted many policies to reduce environmental pollution and carbon dioxide (CO2) emission. This study aimed to determine and understand the relationship between innovation, renewable energy consumption, foreign direct investment inflows, economic growth and CO2 emission in Vietnam. Information was collected annually based on the annual data of the General Statistics Office of Vietnam and World Bank from 2000 to 2022. The authors use the unrestricted fixed effects to resolve the study problems and using panel data method. The results indicate that innovation positively affects environmental pollution in Vietnam. However, renewable energy consumption has a negative effect on environmental pollution in Vietnam. In addition, FDI inflows and economic growth have strongly positive effects on environmental pollution. This paper also provides some recommendations that can assist Vietnam in developing a green and sustainable economy in the technology revolution 5.0 to achieve the United Nations Sustainable Development Goals (SDGs) over a long-term period. [ABSTRACT FROM AUTHOR]

Financial development is often expressed as a private sector initiative earmarked towards motivating economic growth and mitigating poverty. Nonetheless, the need for economic development accompanied by high industrialisation and commercialisation strategies has generated natural environmental effects which have raised enormous concern to green interest groups about whether all the seventeen sustainable development goals will be achieved. This paper investigated the environmental effects of financial development in OECD countries from 2001 to 2012 by employing static models and system GMM analysis. The study utilised foreign direct investments, domestic credit to private sector by banks and domestic credit to private sector as the three proxies of financial development. The effects of these measures of financial development were examined on carbon emissions and greenhouse gases (indicators of environmental quality) and environmental sustainability. In this setting, the findings of the research spotlight that domestic credit to private sector by banks shows a negative and significant relationship with carbon emissions, greenhouse gases and sustainability. Conversely, domestic credit to private sector and economic growth indicates a positive and statistically significant relationship with carbon emissions, greenhouse gases and sustainability. Foreign direct investment is positive and significantly connected with carbon emissions and sustainability but only shows a positive and not significant link with greenhouse gases. The evidence suggested by this analysis adds that the financial system should continue to add more initiatives which consider natural environmental perspectives in their current operations. This present study also confirms the existence of the environmental Kuznets curve (EKC) in carbon emissions (turning point, $109,820), total greenhouse gases (turning point, $74,280) and sustainability (turning point, $112,505). The finding that the turning point of carbon emissions is higher than that of total greenhouse gases indicates why introducing initiatives designed to curb growth of carbon emissions in the respective OECD economies is important. [ABSTRACT FROM AUTHOR]

Global warming, climate change, and resource depletion have forced us to reconsider energy usage and efficiencies over the last few decades. Residential and commercial buildings are both large energy consumers, so improving energy and material usage efficiency in this sector is a common research topic. According to a recent study, the Building Sector (BS) accounts for 40% of greenhouse gas emissions. The primary objective of this paper is to examine and assess the potential of Renewable Energy Systems (RES) and their combinations for enhancing energy efficiency in the BS. Specifically, the focus will be on converting low energy-efficient buildings into highly efficient ones. The potential of the RES and their combinations for the BS is evaluated based on payback durations, energy generation, and reduction of CO 2 emissions. The optimization flow charts for the RES, feasibility studies, commercialization road maps of energy storage systems and the necessity of control mechanisms for enhancing RES efficiency were discussed. Additionally, the technology drawbacks are discussed, along with various innovative techniques recommended to direct future study in this area. Finally, this article assists the audience clear idea in the selection of the right combination of potential RES based on different conditions to achieve deep decarbonization in BSs. • Buildings sector (BS) responsible for 40% of GHG emissions. • Study assesses potential of Renewable Energy System (RES) in BS. • RES evaluated on payback, energy generation, and CO 2 reduction. • Optimization, feasibility and commercialization studies were discussed. • Article recommends right RES combinations for deep decarbonization in BS. [ABSTRACT FROM AUTHOR]

Germany's climate goals require over 75% of its housing stock to be retrofitted for energy efficiency. An important question for Germany and other countries is, what minimum energy efficiency standard should be mandated for housing retrofits so as to reduce CO2 emissions economically efficiently? It has long been known that the marginal costs of energy efficiency increase as standards are pushed higher, and therefore the marginal costs of CO2 abatement increase, but at the same time energy consumption reduces, thereby reducing CO2 emissions. This paper investigates where the social optimum lies between these two effects. In addition to a large database of house sale advertisements containing price and energy efficiency data, it uses two semi-official data sources of modelled and actual energy efficiency upgrades in Germany. It finds that retrofitting to a modestly high standard of about 70 kWh/m2/y entails marginal costs of CO2 abatement comparable to current CO2 prices. However, each step to a higher standard increases these by up to an order of magnitude, with absurdly high marginal costs of CO2 abatement for the highest standards. German policymakers are currently pushing for higher minimum standards, but money could better be spent retrofitting more houses to less stringent standards. • Policymakers seek to drive energy efficiency in buildings to ever higher levels. • But costs of CO2 abatement rise extremely sharply as standards tighten. • Use cost data on retrofits of German buildings to various efficiency standards. • Retrofits to current standards are at the limit of CO2 abatement efficiency. • Driving standards higher increases CO2 abatement cost by an order of magnitude. [ABSTRACT FROM AUTHOR]

Land use carbon emission is one of the main sources of greenhouse gases. In order to achieve carbon reduction, it is crucial to clarify the pathways of land use carbon emission influencing factors. The article takes Chang-Zhu-Tan urban agglomeration as the study area, combines the land use remote sensing data and energy consumption statistics from 1995 to 2018, and uses the STIRPAT extended model to identify the economic, population, energy, technology, policy, land and opening-up level as the latent variables affecting the land use carbon emission in the study area, to establish PLS-SEM to explore the influence of latent variables on dependent variables and the interaction between latent variables. The results show that the seven selected latent variables had good explanatory power on land use carbon emission with R2 reaching 0.983. Economy, population, energy and land — the latent variables in this study — can directly influence land use carbon emission. This study found that land was the most important factor in increasing land use carbon emissions, with a path coefficient of 1.471, while economy played a negative role, with a path coefficient of −1.401. Latent variables other than energy and land all had mediating effects on the dependent variable or the remaining latent variables directly or indirectly. Indirect effects included complete mediation and partial mediation. For example, technology and opening-up level cannot directly influence the dependent variable, but can have a completely indirect effect by affecting energy (complete mediation). The fitness and superiority of the model developed in this paper were extremely high, with values of 0.067 and 0.846 for SRMR and GOF, respectively. This study helps to clarify the influence paths of land use carbon emission and figuratively depicts the path direction and degree of influence among influencing factors, which will help Chang-Zhu-Tan urban agglomeration achieve carbon emission reduction. • Traditional methods to explore influencing factors, such as LMDI, Kaya or STIRPAT model, can only reflect the direct effect of independent variables on dependent variables, thus obtaining the corresponding influenced coefficient. • For instance, Aruhan et al. (2019) used the STIRPAT model to select indicators such as population for linear regression analysis, and explored the influencing factors of land use carbon emission in Toronto, Inner Mongolia. Quan et al. (2020) calculated logistics carbon emissions in China based on panel data. • The study decomposed the data into different factors such as energy intensity by using LMDI decomposition model, and explored their impact on carbon emission alone. Han et al. (2019) established a multivariate linear correlation test model by using Kaya for the construction of new urbanization with independent variables to analyze the influencing factors in China's urbanization construction. • However, the ways in which various factors affect land use carbon emissions are often complex. Factors may affect land use carbon emissions in a direct and/or indirect ways. Indirect effects also mean that there is a mediation effect, which would lead to more detailed and accurate models. • Coincidentally, partial least squares structural equation model (PLS-SEM) can meet this demand, and PLS-SEM can also visualize the action path map, showing the direction and magnitude of the impact on land use carbon emission more straightforwardly. [ABSTRACT FROM AUTHOR]

Cities consume almost 80 percent of world's energy and account for 60 percent of all the emissions of carbon dioxide and significant amounts of other greenhouse gases (GHG). The ongoing rapid urbanization will further increase GHG emissions of cities. The quantification of the environmental impact generated in cities is an important step to curb the impact. In fact, quantifying the consumption activities taking place inside a city, if differentiated by socioeconomic and demographic groups, can provide important insights for sustainable-consumption policies. However, the lack of high-resolution data related to these activities makes it difficult to quantify urban GHG emissions (as well as other impacts). This paper presents a methodology that can quantify the carbon footprint of households in cities using consumption data from a national or European level, where the resource consumption is linked to socioeconomic attributes of a population. The methodology is applied to analyzing the environmental impact by household resource consumption in the city of The Hague in the Netherlands. The key insights reveal potential intervention areas regarding resource consumption categories and demographic groups that can be targeted to reduce GHG emissions due to consumption-driven activities in the city. • Demographic clustering to realize a socio-economic segmentation of the population. • Regression and data-driven models applied at district level to derive resource consumption. • Carbon emissions calculated by matching resource consumption with process-based LCI database. • Mobility, household waste and energy use account for 70% of carbon emissions in The Hague. • An estimated potential reduction of 30% carbon emissions in the mobility sector for The Hague. [ABSTRACT FROM AUTHOR]

To meet the 2050 CO 2 targets, the shipping industry which is responsible for about 3% of global CO 2 emissions needs to be optimized in several aspects. Obviously, alternative fuels constitute the main measure in this respect. However, relatively high fuel prices in combination with increasing political and economic pressure may raise the need for more efficient ship operation. Ship route optimization can make an indispensable contribution to achieving this goal. In this sense, this paper applies an innovative approach for route optimization using Reinforcement Learning (RL). For this purpose, a generic ship model is first developed using Artificial Neural Networks (ANNs) to predict the fuel consumption of the ship. Moreover, various RL methods, namely Deep Q-Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO) are applied. The application of RL enables continuous action space and simultaneous optimization of ship speed and heading. DDPG demonstrates the best results as an off-policy and policy gradient method which allows a continuous action space. For example, in the fuel consumption minimization scenario without time limitation, this method can achieve savings of 6.64%. For DQN as a method with discrete action space, this value is 1.07%. [Display omitted] • ANN models provide an accurate and realistic representation of the ship's fuel consumption. • Reinforcement learning enables continuous action space for route optimization. • DDPG proves to be the best option for ship route estimation with RL. • Compared to the reference conditions, fuel savings of up to 6.64% are achieved. • The advantages of continuous action space over discrete action space are confirmed. [ABSTRACT FROM AUTHOR]

Environmental regulations are an important means for government managers to manage the environment. The motivation of this article is to investigate the influence mechanism of incentive and mandatory environmental regulations on energy efficiency and carbon dioxide (CO 2) emissions in the logistics industry. The quantile regression can estimate the comprehensive effect of explanatory variables on dependent variables, including maximum, minimum, and median. Based on the panel data of China's 30 provinces from 2005 to 2019, this paper adopts quantile regression to simulate the impact of environmental regulations on CO 2 emissions and energy efficiency. The empirical results obtained are as follows: (1) incentive environmental regulations make a greater contribution to CO 2 emission reduction in Ningxia, Qinghai, and Hainan provinces, due to their more aggressive levy of pollution fees. (2) Mandatory environmental regulations contribute the most to CO 2 emission reductions in the 25th-50th percentile provinces, since these provinces issue more environmental decrees. (3) Incentive environmental regulations produce a greater influence on the energy efficiency in the 50th-75th, 75th-90th and upper 90th percentile groups, due to their greater R&D investment. (4) Mandatory environmental regulations have a greater impact on energy efficiency in Xinjiang, Heilongjiang, and Yunnan provinces. The findings can provide empirical support for the government to formulate effective environmental policies. • Incentive environmental regulations have a greater impact on CO 2 emissions in Ningxia, Qinghai, and Hainan. • Mandatory environmental regulations exert a greater effect on CO 2 emissions in the 25th–50th quantile group. • Incentive environmental regulations produce a greater impact on energy efficiency in the high quantile groups. [ABSTRACT FROM AUTHOR]

The submitted study investigates the role of energy use in agriculture and agricultural output in carbon dioxide emissions with the presence of instrumental variables such as rural population and urbanisation. The data set covers 27 European countries during the period 2010-2020. The quantitative approach was applied using cluster analysis with the previous identification of relations between variables by factor analysis. As the second approach, the Two-Stage Least Square (TSLS) model was estimated. Based on the results, three clusters were created. The heatmap demonstrated the similarity between the comprised countries. The most similar countries are Greece and Hungary, while the most different countries are Luxembourg and Malta. Performed TSLS analysis showed that an increase in energy use is associated with an increase in carbon dioxide emissions. On the other hand, greater agricultural output is associated with lower emissions. However, the statistical significance differs across the individual clusters. [ABSTRACT FROM AUTHOR]

The non-metallic minerals industry is the third-largest industrial energy consumer, and it constitutes around 7% of global CO 2 emissions due to the high energy intensity of production processes. Although the substantial improvement of the energy efficiency of this industry in the EU observed since 1995, the energy intensity of this sector is still relatively high. To reach the goals of the Paris Agreement, it is necessary to implement further steps towards the decarbonization of this sector. Previous works do not provide information about research trends in this field or the current development of technologies. To fill this gap, the following research questions have been asked: (Q1) which decarbonization measures are already applied or under development in the non-metallic minerals sector?; (Q2) which of these measures might bring breakthrough to this sector?; (Q3) what is the impact of these measures on costs, energy consumption and GHG emissions? To this end, this paper performs a systematic literature review of GHG emission mitigation options of the non-metallic minerals industry by screening 2964 articles and performing an in-depth analysis of 48 full-text relevant articles following the PRISMA approach. The results show that the carbon capture, utilization and storage (CCUS) is the most popular research topic and has the highest decarbonization potential for the industry (up to 60%). However, more research is needed for the full-scale implementation. Further lowering the clinker to cement ratio to at least 75% is also of major importance, however, it is a mature technology, and not much research is required in this field. Despite the relatively low representation in scientific literature, heat electrification is an unavoidable direction for the industry in the long term, resulting from the global shift towards sustainable energy from renewables. The article also sets directions for future research, especially in terms of further development of the most promising technologies and on the industry transition pathways that would be legally adopted at the international level. • The non-metallic minerals industry constitutes around 7% of global CO 2 emissions. • A systematic literature review of GHG emission mitigation options of the industry. • 2964 articles screened and 48 full-text articles analyzed. • CCS is the most discussed mitigation option, yet not ready for commercialization. • Heat electrification is an unavoidable direction, resulting from the climate policy. [ABSTRACT FROM AUTHOR]

This study was aimed to determine the energy use and equivalent carbon dioxide emissions (CO2-equivalent) in per unit production area (ha) considering the petroleum products (PU) directly used for the production of table tomatoes in open field conditions in Adana province. Energy consumption values related to diesel fuel and engine oil (PU) usage were calculated based on the quantities used for the unit area (ha) of PUs and lower energy values (MJ/L), which are an important indicator of the energy content of these products (PU). As the carbon dioxide emission (kg CO2) related tothe use of PU, kg CO2 values are taken into consideration depending on the energy content (MJ) of the PU. A total of 249.6 L/ha diesel fuel is consumed when using tools and machinery in tomato production. As a result of diesel fuel consumption, a total of 685.34 kg CO2/ha CO2 emission occurs in tomato production. As a result of engine oil consumption in open field tomato production, using tools and machinery, a total of 0.1153 kg CO2/ha CO2 emission occurs. [ABSTRACT FROM AUTHOR]