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The severe global warming issue currently threatens humans' existence and development. Countries and international organizations have effectively implemented policies to reduce carbon emissions and investigate low-carbon growth strategies. Reducing carbon emissions is a hot topic that academics and government policy-making departments are concerned about.Through necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis(fsQCA), this paper investigates local governments' configuration linkage effect and path choice to improve carbon emission performance from six dimensions: energy consumption, industrial structure, technological innovation, government support, economic development, and demographic factors. The research findings include the following: (1) Individual condition does not represent necessary conditions for the government's carbon performance. Among the two sets of second-order equivalence configurations(S and Q) (five high-level carbon performance configurations), those dominated by economic development or low energy consumption can produce high-level carbon performance. Therefore, the six antecedent conditions dimensions work together to explain how the government can create high levels of carbon performance. (2)According to the regional comparison, China's eastern, central, and western regions exhibit similarities and differences in the driving forces behind high carbon emission performance. All three regions can demonstrate carbon emission performance when all the factors are combined. However, when constrained by the conditions of each region's resource endowment, the eastern region emphasizes the advantage of economic and technological innovation, the central region favors government support and demographic factors, and the western region prefers upgrading industrial structure based on a specific level of economic development. [ABSTRACT FROM AUTHOR]

In response to the challenges of global warming and the development of A low-carbon economy, the integrated electricity and natural gas energy system (IEGES) is known as an important structure for future energy supply; thus, its planning and design must take low-carbon and environmental protection factors into account. Regarding carbon emissions as an optimization criterion, this paper built life-cycle carbon emission models of IEGES components. Then, taking the capacities of the energy resources, storage and conversion units of IEGES as the optimization variables, a multi-objective optimization configuration model was established considering the annual investment operation cost and the life-cycle carbon emissions. The multi-objective model was transformed into a single-objective one by an ε-constraint approach and the polynomial fitting method was employed to obtain the value of ε for obtaining uniformly distributed Pareto sets. Based on the fuzzy entropy weight method and the fuzzy affiliation degree approach, the obtained Pareto sets were ranked and the solution with the highest ranking value was selected as the optimal solution for the original problem. Finally, the configuration schemes were analyzed from the perspectives of economy, carbon emission and renewable energy utilization, and the effectiveness and rationality of the proposed optimization method were verified through MATLAB simulation. [ABSTRACT FROM AUTHOR]

Despite the serious threat of global warming caused by carbon emissions, commercial refrigeration systems that generate cooling are unable to adjust their usage time. Therefore, it is essential to enhance the energy efficiency of refrigeration systems themselves. Recently developed refrigeration systems offer improved energy consumption efficiency as they utilize inverter-type outdoor units that can communicate with indoor units. However, traditional legacy refrigeration systems still suffer from poor energy efficiency because the existing indoor units cannot communicate with inverter-type outdoor units. Hence, this paper introduces a heuristic energy efficiency improvement algorithm for commercial refrigeration systems using legacy indoor units when an inverter-type outdoor unit is installed. In particular, to reduce the computational complexity of the process of selecting the optimal temperature difference and target low-pressure variables that can enhance energy efficiency, the algorithm employs the Monte Carlo method. Finally, the performance of the proposed heuristic energy efficiency improvement algorithm was evaluated in a laboratory environment to confirm its applicability. [ABSTRACT FROM AUTHOR]

Blockchain technology is being looked at to solve numerous real-world problems that demand transparency by meeting sustainable goals. Do we ponder whether this technology is a boon or a bane for the environment? This paper analyses blockchain's dominant consensus method, Proof-of-Work (PoW), which consumes more energy than Malaysia and Sweden and further deteriorates the environment through carbon emissions. This study is the first systematic evaluation of PoW consensus-based blockchain applications' environmental consequences. We found 11 significant Theories, 6 Contexts, and 26 Methodologies (TCM) in 60 reviewed articles. We propose an Antecedents, Drivers, and Outcomes (ADO) model, which depicts that marginal profits drive high energy consumption and carbon emissions, with non-renewable energy proportionally responsible for carbon emissions. The article distinctively uses an integrated TCM-ADO framework for literature synthesis and the PESTLE framework for reporting future research areas. This is the first study to use the following four frameworks: PRISMA; TCM; ADO; and PESTLE for systematic literature review. Profit is identified as one of the most significant drivers of energy consumption and further carbon emissions. The article proposes 65 future research areas and makes theoretical contributions to the literature that may interest academicians, practitioners, and social stakeholders. [ABSTRACT FROM AUTHOR]

In response to global warming, China has formulated the "double carbon" strategic goals of peaking carbon dioxide emissions before 2030 and reaching carbon neutrality before 2060. The problem of rural carbon emissions is often ignored due to underdeveloped industries and services. In this paper, the carbon emission pathways in the rural areas of Guangdong Province are investigated. Since energy consumption is the main source of carbon emissions, the factor analysis was used to analyze the main factors affecting rural household energy consumption and agricultural production energy consumption. Multiple linear regression was conducted to predict the rural energy consumption demand in Guangdong. Furthermore, the current situation and development trend of rural energy supply, demand and consumption structure, and the potential of renewable energy development were considered to predict carbon emissions in the rural areas of Guangdong. Moreover, the carbon emission pathways in the rural areas of Guangdong were discussed under two scenarios: the base scenario and the radical model. [ABSTRACT FROM AUTHOR]

The paper presents a comparison of the fuel oil (FO) consumption and carbon dioxide (CO2) emissions of a container ship's 8000 twenty-foot equivalent unit (TEU) during oceanographic navigation. The evaluation has two types of FOs: a 3.4% heavy fuel oil with desulfurization (HFOWD) and a 0.5% very-low-sulfur fuel oil (VLSFO), based on the sulfur cap policy of the International Maritime Organization (IMO). The results show the average FO consumption at 130 tons/day of HFOWD and 141 tons/day of VLSFO, which means shifting to VLSFO increases fuel consumption 8.4% more than the HFOWD. The average CO2 emissions are 429 tons/day of the HFOWD and 471 tons/day of the VLSFO, indicating an 9.5% increase in CO2 emissions when the IMO adopts the low-sulfur fuel policy. Moreover, the VLSFO blending of various chemicals further deteriorates and wears out the main engine of the ship. IMO's low-sulfur fuel policy significantly reduced the emission of sulfur oxides (SOX) and particulate matter emissions. Still, we should not ignore the fact that adopting VLSFO may cause more CO2 emissions. Therefore, while switching to low-sulfur fuels, the maritime industry should improve the related energy efficiency to reduce fuel consumption and CO2 emissions. [ABSTRACT FROM AUTHOR]

The rapid development of public buildings has greatly increased the country's energy consumption and carbon emissions. Excessive carbon emissions contribute to global warming. This paper aims to measure the carbon emissions in the operation of public buildings, and to identify the multiple influencing factors of carbon emissions in operational public buildings. First, the spatial and temporal variation characteristics of carbon emissions from public buildings in 30 provinces of China from 2008–2019 are analyzed. Second, a green building index is constructed, and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model is utilized to explore the relationship between each influencing factor and carbon emissions, using spatial and temporal geographically weighted regression analysis. The results show that the effects of population, urbanization rate, GDP per capita, green building index, and industrial structure on carbon emissions from public buildings all show spatial correlation and differences. There are east-west differences in the operational carbon emissions of public buildings in China's provinces. Cluster evolution shows a spatially increasing trend from west to east. To some extent, policymakers can develop appropriate policies for different provinces through the findings. [ABSTRACT FROM AUTHOR]

Tourism serves as a crucial means of funding national development and ensuring the sustainability of local livelihoods in growing countries such as Brazil. Nevertheless, the accelerated growth of tourism in various nations might lead to significant environmental consequences due to heightened energy consumption. This surge in energy usage contributes to the exacerbation of global warming through the amplified release of carbon dioxide (CO2). In light of the increasingly evident impacts of climate change on the natural environment, a multitude of endeavors have been undertaken with the aim of attaining ecological sustainability. The objective of this study is to examine the dynamic effects of economic growth, energy consumption, and tourism on carbon emissions in Brazil by analyzing time series data spanning from 1990 to 2019. The stationarity of data was assessed through the application of unit root tests, while an autoregressive distributed lag (ARDL) approach was employed to analyze the relationship between the components, accounting for both the long-term and short-term dynamics. The empirical results indicate that a 1% rise in economic growth, energy consumption, and tourist arrivals leads to environmental deterioration by causing a respective increase in CO2 emissions of 1.37%, 1.06%, and 0.57% in the near term, and 0.72%, 0.62%, and 0.16% in the long term. This article presents policy ideas aimed at achieving carbon neutrality in Brazil by increasing the utilization of renewable energy sources, while simultaneously fostering sustainable tourism as a means to enhance the country's economy. Highlights •This study investigated the tourism-economy-energy-environment nexus in Brazil. •The econometric analysis revealed that tourism, energy use, and economic growth increase CO2 emissions. •This article offers policy suggestions to achieve sustainable tourism and carbon neutrality. [ABSTRACT FROM AUTHOR]

In recent years, China has overtaken the United States as the world's largest carbon dioxide (CO2) emitter. CO2 emissions from high-energy-intensive industries account for more than three-quarters of the total industrial carbon dioxide emissions. Therefore, it is important to enhance our understanding of the main factors affecting carbon dioxide emissions in high-energy-intensive industries. In this paper, we firstly explore the main factors affecting CO2 emissions in high-energy-intensive industries, including industrial structure, per capita gross domestic product (GDP), population, technological progress and foreign direct investment. To achieve this, we rely on exploratory regression combined with the threshold criteria. Secondly, a geographically weighted regression model is employed to explore local-spatial heterogeneity, capturing the spatial variations of the regression parameters across the Chinese provinces. The results show that the growth of per capita GDP and population increases CO2 emissions; by contrast, the growth of the services sector's share in China's gross domestic product could cause a decrease in CO2 emissions. Effects of technological progress on CO2 emissions in high-energy-intensive industries are negative in 2007 and 2013, whereas the coefficient is positive in 2018. Throughout the study period, regression coefficients of foreign direct investment are positive. This paper provides valuable insights into the relationship between driving factors and CO2 emissions, and also gives provides empirical support for local governments to mitigate CO2 emissions. [ABSTRACT FROM AUTHOR]

The increasing contribution of refrigeration systems to global warming has led to widespread demand for low global warming potential (GWP) refrigerants as alternatives. This paper proposed a comprehensive environmental assessment method for evaluating the carbon footprint of units charged with low-GWP refrigeration. Experimental and numerical studies were used to investigate the carbon footprint impact of using alternative refrigerants at different ambient temperatures in China. Initially, the model was validated through experimental case studies on the use stage of cold storage units. Subsequently, a comparison was made between the cooling capacity (CC) and energy efficiency ratio (EER) of cold storage units charged with different refrigerants. Additionally, the impact of alternative refrigerants in the use stage was analyzed by numerical calculations, and the adaptability of the cold storage unit to the ambient temperature and the national energy structure was also studied. The results indicate that among the four stages of the cold storage unit, the use stage is the hotspot of carbon footprint due to the large amount of energy consumption and the existence of refrigerant leakage and other behaviors. Utilizing low-GWP alternative refrigerants could significantly reduce the carbon footprint of the cold storage units' use stage by 10.56%–16.39%, thereby lowering the whole stage carbon footprint. Furthermore, the ambient temperature and national energy structure could also effectively reduce the carbon footprint of cold storage units' whole stage by 1%–27.17%. Furthermore, after adopting low GWP refrigerants, the life-cycle cost (LCC) of the units have also decreased by 6.1%–12%. Taking into account both environmental impact and economic considerations, it is recommended to directly replace R404A with R448A in the units in regions where the average ambient temperature is above 15 °C. • A carbon footprint evaluation model for the units was established. • Influencing factors of carbon footprint are studied numerically and experimentally. • By GHG assessments, the optimal refrigerant and external conditions were determined. • Alternative refrigerants reduce carbon emissions for cold storage units by 8.8%–24.92%. [ABSTRACT FROM AUTHOR]

Nowadays, the methodology aiming to achieve carbon neutrality and net zero energy on a large scale is known. Despite this, few specialists are mastering this technology globally. What new scenarios. applied at the neighbourhood scale. generate a significant reduction in the rate of CO2 emissions and energy demand? In addition, a lack of massive, regular, and consistent data on carbon emissions and energy consumption has made it significantly difficult to understand the origins of climate change at the building and neighbourhood scales. This work has, as its main goal, the assessment of different strategies that facilitate reduction in the concentration of CO2 and lower energy demands at the district level. The life cycle assessment of a new district has been carried out over 100 years during the four stages of the life cycle of the neighbourhood (construction, operation, demolition and end of life). The results showed that up to 93% of greenhouse gas (GHG) was produced during the operational stage. The energy demand due to transport and waste management represented 60% of the total energy demand of the district during the operational stage. There is still a possibility to maintain air temperature growth around 1.5 °C in the next decade by means of the following: Global warming + 100% of heavy renovation of all buildings + 100% of electric car − renewable energy. This strategy would facilitate a reduction of over 92% of the CO2 produced at the district level. [ABSTRACT FROM AUTHOR]

The power sector is a major contributor to anthropogenic global warming and is responsible for 38% of total energy-related carbon dioxide emissions and 66% of carbon dioxide emission growth in 2018. In OECD member countries, the residential sector consumes a significant amount of electrical energy, with household refrigerating appliances alone accounting for 30–40% of the total consumption. To analyze the energy use of each domestic appliance, researchers have developed Appliance-Level Energy Characterization (ALEC), a set of techniques that provide insights into individual energy consumption patterns. This study proposes a novel methodology that utilizes robust probability density estimation to detect refrigerators with high energy consumption and recommend tailored energy-saving measures. The methodology considers two consumption features: base energy consumption (energy usage without human interaction) and relative energy consumption (energy usage influenced by human interaction). To assess the approach's effectiveness, the methodology was tested on a dataset of 30 different appliances from monitored homes, yielding positive results that support the robustness of the proposed method. [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]

Global warming is directly related to heavy-duty vehicle fuel consumption and greenhouse gas (CO2 mainly) emissions, which, in China, are certified on the vehicle chassis dynamometer. Currently, vast amounts of vehicle real-road data from the portable emission measurement system (PEMS) and remote monitoring are being collected worldwide. In this study, a binning-reconstruction calculation model is proposed, to predict the chassis dynamometer fuel consumption and CO2 emissions with real-road data, regardless of operating conditions. The model is validated against chassis dynamometer and PEMS test results, and remote monitoring data. Furthermore, based on the proposed model, the fuel consumption levels of 1408 heavy-duty vehicles in China are analyzed, to evaluate the challenge to meet the upcoming China fourth stage fuel consumption limits. For accumulated fuel consumption based on the on-board diagnostic (OBD) data stream, a predictive relative error less than 5% is expected for the present model. For bag sampling results, the proposed model's accuracy is expected to be within 10%. The average relative errors between the average fuel consumption and the China fourth stage limits are about 3%, 8%, and 0.7%, for current trucks, tractors, and dump trucks, respectively. The urban operating condition, with lower vehicle speeds, is the main challenge for fuel consumption optimization. [ABSTRACT FROM AUTHOR]

Global warming is a serious problem presently faced by human society, and CO 2 is a major concern as a greenhouse gas (GHG), especially as the amount of atmospheric CO 2 has been steadily increasing in recent years. Multi-depot model of vehicle routing has sparked widespread interest among researchers as an effective means of reducing cost comprising carbon emissions, fuel consumption, vehicles rental, and driver salaries. In this paper, we developed an improved adaptive large neighborhood search (ALNS) algorithm to efficiently solve large-scale instances of the multi-depot green vehicle routing problem with time windows (MDGVRPTW), based on the characteristics of the multi-depot model, and the formulation of carbon emissions from customers. For the destroy operation, three problem-specific destroy operators that took advantage of the structure of the multi-depot model and the calculation formula of carbon emissions, were deliberately tailored for the MDGVRPTW. The operators could rapidly remove customers causing significant carbon emissions, which enabled these customers to be better positioned in future repair operations. For the repair operation, a noise-greedy repair operator was proposed to enhance the diversification capabilities of the ALNS. Additionally, two methods for speeding up the repair process were proposed to improve computational time. Computational experiments revealed that the proposed ALNS algorithm exhibited a significant improvement in calculation speed and accuracy compared with existing algorithms. • Multi-depot green VRPTW (MDGVRPTW) to reduce emissions and economic cost is studied. • Improved adaptive large neighborhood search (ALNS) is devised for larger-scale MDGVRPTW. • Three novel destroy operators and two speeding-up repair methods are developed. • Accuracy is improved by 17.94% on average compared to classic ALNS. [ABSTRACT FROM AUTHOR]

Research background: CO2 emissions are considered to be the main reason for global warming, and for this reason, their regulation is a very important issue for governments. Due to the increasing use of energy, carbon dioxide emissions have increased dramatically over the past century, with a direct link to economic growth and development. The relationship between CO2 emissions, growth and energy consumption is therefore at the heart of current economic issues. Purpose: This study aimed at examining the relationship among economic growth, carbon dioxide (CO2) emissions and energy consumption in selected MENA countries, in the period 1995–2017. Research methodology: To prove these relations, a stationary data panel methodology is used supported by unitary root and cointegration tests. Results: The results indicated that there is a long-term relationship between CO2 emissions, energy consumption and GDP. In addition, it is found that the elasticity of CO2 emissions with respect to energy consumption is less than one (inelastic), and the elasticity of CO2 emissions with respect to GDP suggests the existence of an Environmental Kuznets Curve. An important finding is that energy consumption has a positive but relatively low effect on CO2 emissions. To reduce CO2 emissions, the countries of the MENA region are being called upon to increase significantly the use of renewable energies and the establishment of a more efficient energy policy. [ABSTRACT FROM AUTHOR]