Search Results (889)

Interactions between the increased penetration of the power system by renewable energy sources (RESs) and the energy pricing mechanism in the EU (day-ahead market) can lead to many unexpected and paradoxical consequences. This article analyses the case of the long-term maintenance of prices around zero on the day-ahead market in south-western Europe at a certain time of a day. This is an important case since, at the same time, this area generates electricity from a similar source mix as it is in the target for the EU. Zero or very low energy prices are becoming increasingly common across the EU. This can pose a problem for the stability of the electricity supply, as it translates into a lower power of used disposable power sources, which can be used as a reserve when the majority of the energy supply comes from renewable energy sources. Furthermore, this work refutes the most frequently proposed solution to the problem of excessively low prices based on energy storage systems. This work attempts to analyze the long-term low-price situation in Spain and extrapolate the expected consequences based on it; however, it is difficult to find all the factors that occur in the power system and influence the price market and vice versa. The issue is multidimensional and complex, and the analyzed situation revealed a number of trends. Therefore, a multifaceted problem remains. A constant electricity supply must be ensured at a reasonable price, thus avoiding the exposure of individual consumers to energy shortages or significant price increases, while, at the same time, the EU must reduce dependence on fossil fuels, and its legislation must push for reduced CO₂ emissions. On the other hand, the EU must provide some type of market mechanism to support the achievement of these goals because the current pricing mechanism based on the day-ahead market does not seem to be effective. This article aims to spark a discussion about this problem; it does not provide any simple solutions to it. Full article

In the context of both domestic and international economic landscapes, regional policy has emerged as an increasingly influential factor shaping the developmental trajectories of Chinese enterprises. Despite its growing significance, the extant literature lacks a comprehensive and systematically visualized synthesis that encapsulates the scope and trends of research in this domain. This study addresses this critical gap by conducting an integrative bibliometric and qualitative review of the academic output related to regional policy and Chinese firm growth. Drawing on a final dataset comprising 3428 validated academic publications—selected from an initial pool of 3604 records retrieved from the Web of Science Core Collection between 1991 and 2022, the research employs a two-stage methodological framework. In the first phase, advanced bibliometric tools, and software applications, including RStudio, Bibliometrix, VOSviewer, and CitNetExplorer, are utilized to implement techniques such as keyword co-occurrence analysis, thematic clustering, and the tracing of thematic evolution over time. These methods facilitate rigorous data cleansing, breakpoint identification, and the visualization of intellectual structures and emerging research patterns. In the second phase, a targeted qualitative review is conducted to evaluate the influence of regional policies on Chinese firms across three critical stages of business development: start-up, expansion, and maturity. The findings reveal that regional policy interventions generally exert a positive influence on firm performance throughout all stages of development. Notably, a significant concentration of citation activity occurred prior to 2017; however, post-2017, the volume of scholarly publications, journal-level impact (as measured by h-index), and author-level influence experienced a marked increase. Among the 3428 analyzed publications, a substantial portion—2259 articles—originated from Chinese academic institutions, highlighting the strong domestic research interest in the subject. Furthermore, since 2015, there has been a discernible shift in keyword co-occurrence trends, with increasing scholarly attention directed towards sustainable development issues, particularly those related to carbon dioxide emissions and green innovation, reflecting evolving policy priorities and environmental imperatives. Full article

Understanding the carbon emission efficiency of apple production (APCEE) is critical for promoting green and low-carbon agricultural development. However, the spatiotemporal dynamics and driving factors of APCEE in China remain inadequately explored. This study employs life cycle assessment, super-efficiency slacks-based measures, and a panel Tobit model to evaluate the carbon footprint, APCEE, and its determinants in China’s two major production regions from 2003 to 2022. The results reveal that: (1) Producing one ton of apples in China results in 0.842 t CO₂e emissions. Land carbon intensity and total carbon emissions peaked in 2010 (28.69 t CO₂e/ha) and 2014 (6.52 × 10⁷ t CO₂e), respectively, exhibiting inverted U-shaped trends. Carbon emissions from various production areas show significant differences, with higher pressure on carbon emission reduction in the Loess Plateau region, especially in Gansu Province. (2) The APCEE in China exhibits a W-shaped trend (mean: 0.645), with overall low efficiency loss. The Bohai Bay region outperforms the Loess Plateau and national averages. (3) The structure of the apple industry, degree of agricultural mechanization, and green innovation positively influence APCEE, while the structure of apple cultivation, education level, and agricultural subsidies negatively impact it. Notably, green innovation and agricultural subsidies display lagged effects. Moreover, the drivers of APCEE differ significantly between the two major production regions. These findings provide actionable pathways for the green and low-carbon transformation of China’s apple industry, emphasizing the importance of spatially tailored green policies and technology-driven decarbonization strategies. Full article

Peatlands store substantial amounts of carbon (C) in the form of peat, but are increasingly threatened by drought and shrub encroachment under climate warming. However, how peat decomposition and its temperature sensitivity (Q₁₀) vary with depth and plant litter input under these stressors remains poorly understood. We incubated peat from two depths with different degrees of decomposition, either alone or incubated with Sphagnum divinum shoots or Betula ovalifolia leaves, under five temperature levels and two moisture conditions in growth chambers. We found that drought and Betula addition increased CO₂ emissions in both peat layers, while Sphagnum affected only shallow peat. Deep peat alone or with Betula exhibited higher Q₁₀ than pure shallow peat. Drought increased the Q₁₀ of both depths’ peat, but this effect disappeared with fresh litter addition. The CO₂ production rate showed a positive but marginal correlation with microbial biomass carbon, and it displayed a rather similar responsive trend to warming as the microbial metabolism quotient. These results indicate that both deep and dry peat are more sensitive to warming, highlighting the importance of keeping deep peat buried and waterlogged to conserve existing carbon storage. Additionally, they further emphasize the necessity of Sphagnum moss recovery following vascular plant encroachment in restoring carbon sink function in peatlands. Full article

The intersection of green finance and artificial intelligence (AI) represents a rapidly emerging and high-impact research domain with the potential to reshape sustainable economic systems. This study presents a comprehensive bibliometric and network analysis aimed at mapping the scientific landscape, identifying research hotspots, and highlighting methodological trends at this nexus. A dataset of 268 peer-reviewed publications (2014–June 2025) was retrieved from the Web of Science Core Collection, filtered by the Business Economics category. Analytical techniques employed include Bibliometrix in R, VOSviewer, and science mapping tools such as thematic mapping, trend topic analysis, co-citation networks, and co-occurrence clustering. Results indicate an annual growth rate of 53.31%, with China leading in both productivity and impact, followed by Vietnam and the United Kingdom. The most prolific affiliations and authors, primarily based in China, underscore a concentrated regional research output. The most relevant journals include Energy Economics and Finance Research Letters. Network visualizations identified 17 clusters, with focused analysis on the top three: (1) Emission, Health, and Environmental Risk, (2) Institutional and Technological Infrastructure, and (3) Green Innovation and Sustainable Urban Development. The methodological landscape is equally diverse, with top techniques including blockchain technology, large language models, convolutional neural networks, sentiment analysis, and structural equation modeling, demonstrating a blend of traditional econometrics and advanced AI. This study not only uncovers intellectual structures and thematic evolution but also identifies underdeveloped areas and proposes future research directions. These include dynamic topic modeling, regional case studies, and ethical frameworks for AI in sustainable finance. The findings provide a strategic foundation for advancing interdisciplinary collaboration and policy innovation in green AI–finance ecosystems. Full article

The priming effect (PE), a microbially mediated process, critically regulates the balance between carbon sequestration and mineralization. This study used soils from different soil depths (0–20 cm, 20–40 cm, and 40–60 cm) under Picea schrenkiana forest in the Tianshan Mountains as the research object. An indoor incubation experiment was conducted by adding three concentrations (1% SOC, 2% SOC, and 3% SOC) of ¹³C-labelled glucose. We applied ¹³C isotope probe-phospholipid fatty acid (PLFA-SIP) technology to investigate the influence of readily labile organic carbon inputs on soil priming effect (PE), microbial community shifts at various depths, and the mechanisms underlying soil PE. The results indicated that the addition of ¹³C-labeled glucose accelerated the mineralization of soil organic carbon (SOC); CO₂ emissions were highest in the 0–20 cm soil layer and decreased trend with increasing soil depth, with significant differences observed across different soil layers (p < 0.05). Soil depth had a positive direct effect on the cumulative priming effect (CPE); however, it showed negative indirect effects through physico-chemical properties and microbial biomass. The CPE of the 0–20 cm soil layer was significantly positively correlated with ¹³C-Gram-positive bacteria, ¹³C-Gram-negative bacteria, and ¹³C-actinomycetes. The CPE of the 20–40 cm and 40–60 cm soil layers exhibited a significant positive correlation with cumulative mineralization (CM) and microbial biomass carbon (MBC). Glucose addition had the largest and most significant positive effect on the CPE. Glucose addition positively affected PLFAs and particularly microbial biomass. This study provides valuable insights into the dynamics of soil carbon pools at varying depths following glucose application, advancing the understanding of forest soil carbon sequestration. Full article

The rice-wheat rotation is the main agricultural cropping system in Jiangsu Province, playing a vital role in ensuring food security and promoting economic development. However, current research on rice-wheat systems mainly focuses on in-situ controlled experiments at the point scale, with limited studies addressing carbon footprint (CF) and energy balance (EB) at the regional scale and long time series. Therefore, we analyzed the evolution patterns of the CF and EB of the rice-wheat system in Jiangsu Province from 1980 to 2022, as well as their influencing factors. The results showed that the sown area and total yield of rice and wheat exhibited an increasing–decreasing–increasing trend during 1980–2022, while the yield per unit area increased continuously. The CF of rice and wheat increased by 4172.27 kg CO₂ eq ha⁻¹ and 2729.18 kg CO₂ eq ha⁻¹, respectively, with the greenhouse gas emissions intensity (GHGI) showing a fluctuating upward trend. Furthermore, CH₄ emission, nitrogen (N) fertilizer, and irrigation were the main factors affecting the CF of rice, with proportions of 36%, 20.26%, and 17.34%, respectively. For wheat, N fertilizer, agricultural diesel, compound fertilizer, and total N₂O emission were the primary contributors, accounting for 42.39%, 22.54%, 13.65%, and 13.14%, respectively. Among energy balances, the net energy (NE) of rice exhibited an increasing and then fluctuating trend, while that of wheat remained relatively stable. The energy utilization efficiency (EUE), energy productivity (EPD), and energy profitability (EPF) of rice showed an increasing and then decreasing trend, while wheat decreased by 46.31%, 46.31%, and 60.62% during 43 years, respectively. Additionally, N fertilizer, agricultural diesel, and compound fertilizer accounted for 43.91–45.37%, 21.63–25.81%, and 12.46–20.37% of energy input for rice and wheat, respectively. Moreover, emission factors and energy coefficients may vary over time, which is an important consideration in the analysis of long-term time series. This study analyzes the ecological and environmental effects of the rice-wheat system in Jiangsu Province, which helps to promote the development of agriculture in a green, low-carbon, and high-efficiency direction. It also offers a theoretical basis for constructing a low-carbon sustainable agricultural production system. Full article

With the ongoing advancement of industrialization and rapid urbanization, the emission of volatile organic compounds (VOCs) has increased significantly. As key precursors of PM_2.5 and ozone formation, VOCs pose a growing threat to the health of ecosystems. Due to their complex and dynamic transformation processes across air, water, and soil media, the ecological risks associated with VOCs have attracted increasing attention from both the scientific community and policy-makers. This study systematically reviews the core literature on the ecological impacts of VOCs published between 2005 and 2024, based on data from the Web of Science and Google Scholar databases. Utilizing three bibliometric tools (CiteSpace, VOSviewer, and Bibliometrix), we conducted a comprehensive visual analysis, constructing knowledge maps from multiple perspectives, including research trends, international collaboration, keyword evolution, and author–institution co-occurrence networks. The results reveal a rapid growth in the ecological impact of VOCs (EIVOCs), with an average annual increase exceeding 11% since 2013. Key research themes include source apportionment of air pollutants, ecotoxicological effects, biological response mechanisms, and health risk assessment. China, the United States, and Germany have emerged as leading contributors in this field, with China showing a remarkable surge in research activity in recent years. Keyword co-occurrence and burst analyses highlight “air pollution”, “exposure”, “health”, and “source apportionment” as major research hotspots. However, challenges remain in areas such as ecosystem functional responses, the integration of multimedia pollution pathways, and interdisciplinary coordination mechanisms. There is an urgent need to enhance monitoring technology integration, develop robust ecological risk assessment frameworks, and improve predictive modeling capabilities under climate change scenarios. This study provides scientific insights and theoretical support for the development of future environmental protection policies and comprehensive VOCs management strategies. Full article

Household appliances constitute the second largest source of residential energy consumption in China, accounting for over 20% of the total and exhibiting a steady growth trend. Despite their substantial impact on energy demand and carbon emissions, a comprehensive analysis of the current status and future trends of household appliances in China is still lacking. This study employs the Long-Range Energy Alternatives Planning (LEAP) system to model energy consumption and carbon emissions for five major household appliances (air conditioners, refrigerators, washing machines, TVs, and water heaters) from 2022 to 2052. Three scenarios were analyzed: a Reference (REF) scenario (current trends), an Existing Policy Option (EPO) scenario (current energy-saving measures), and a Further Strengthening (FUR) scenario (enhanced efficiency measures). Key results show that by 2052, the EPO scenario achieves cumulative savings of 1074.8 billion kWh and reduces emissions by 580.7 million metric tons of CO₂ equivalent compared to REF. The FUR scenario yields substantially greater benefits, demonstrating the significant potential of strengthened policies. This analysis underscores the critical role of improving appliance energy efficiency and provides vital insights for policymakers and stakeholders aiming to reduce residential sector emissions. Full article

The current research trends of production engineering are based on optimizing the machining process concerning human and environmental factors. High-performance materials, such as hardened steels, nickel-based alloys, fiber-reinforced polymer (FRP) composites, and titanium alloys, are classified as hard-to-cut due to their ability to maintain strength at high operating temperatures. Due to these characteristics, such materials are employed in applications such as aerospace, marine, energy generation, and structural. The purpose of this article is to investigate the machinability of these alloys under various cutting conditions. The purpose of this article is to compare cryogenic cooling and cryogenic processing from the perspective of machinability and sustainability in the manufacturing process. Compared to conventional machining, hybrid techniques, which mix cryogenic and minimal quantity lubricant, led to significantly reduced cutting forces of 40–50%, cutting temperatures and surface finishes by approximately 20–30% and more than 40%, respectively. A carbon footprint is determined by several factors including power consumption, energy requirements, and carbon dioxide emissions. As a result of the cryogenic technology, the energy consumption, power consumption, and CO₂ emissions were reduced by 40%, 28%, and 35%. Full article

In recent years, carbon dioxide (CO₂) emissions have increased at the fastest rates ever recorded. This is a trend that contradicts global efforts to stabilise greenhouse gas (GHG) concentrations and prevent long-term climate change. Over 90% of global transport relies on oil-based fuels. The continued use of diesel and petrol raises concerns related to oil costs, supply security, GHG emissions, and the release of air pollutants and volatile organic compounds. This study explored electric vehicle (EV) charging networks by assessing environmental impacts through GHG and petroleum savings, developing dynamic pricing strategies, and forecasting infrastructure needs. A substantial dataset of over 259,000 EV charging records from Palo Alto, California, was statistically analysed. Machine learning models were applied to generate insights that support sustainable and economically viable electric transport planning for policymakers, urban planners, and other stakeholders. Findings indicate that GHG and gasoline savings are directly proportional to energy consumed, with conversion rates of 0.42 kg CO₂ and 0.125 gallons per kilowatt-hour (kWh), respectively. Additionally, dynamic pricing strategies such as a 20% discount on underutilised days and a 15% surcharge during peak hours are proposed to optimise charging behaviour and improve station efficiency. Full article

In the United States, ecosystems regularly experience wildfires and as fire seasons lengthen, fires are becoming a more important disturbance. While all types of disturbance have impacts on the carbon cycle, fires result in immediate emissions into the atmosphere. To assist managers in assessing wildland fire impacts, particularly on federally managed land, we developed estimates of area burned and related emissions for a 21-year period. These estimates are based on wildland fires defined by the interagency Monitoring Trends in Burn Severity database; emissions are simulated through the Wildland Fire Emissions Inventory System; and the classification of public land is performed according to the US Geological Survey’s Protected Areas Database of the United States. Wildland fires on federal land contributed 62 percent of all annual CO₂ emissions from wildfires in the United States between 2001 and 2021. During this period, emissions from the forest fire subset of wildland fires ranged from 328 Tg CO₂ in 2004 to 37 Tg CO₂ in 2001. While forest fires averaged 38 percent of burned area, they represent the majority—59 to 89 percent of annual emissions—relative to fires in all ecosystems, including non-forest. Wildland fire emissions on land belonging to the federal government accounted for 44 to 77 percent of total annual fire emissions for the entire United States. Land managed by three federal agencies—the Forest Service, the Bureau of Land Management, and the Fish and Wildlife Service—accounted for 93 percent of fire emissions from federal land over the course of the study period, but year-to-year contributions varied. Full article

In Japan’s hilly and mountainous areas, which cover over 60% of the national land area, issues such as population outflow, aging, and regional decline are intensifying. This study explored sustainable decarbonization pathways by examining two representative regions (Maniwa City and Hidakagawa Town), while accounting for diverse regional characteristics. A bottom-up approach was adopted to calculate energy consumption and CO₂ emissions within residential and transportation sectors. Six future scenarios were developed to evaluate emission trends and countermeasure effectiveness in different regions. The key findings are as follows: (1) in the study areas, complex regional issues have resulted in relatively high current levels of CO₂ emissions in these sectors, and conditions may worsen without intervention; (2) if the current trends continue, per-capita CO₂ emissions in both regions are projected to decrease by only around 40% by 2050 compared to 2020 levels; (3) under enhanced countermeasure scenarios, CO₂ emissions could be reduced by >99%, indicating that regional decarbonization is achievable. This study provides reliable information for designing localized sustainability strategies in small-scale, under-researched areas, while highlighting the need for region-specific countermeasures. Furthermore, the findings contribute to the realization of multiple Sustainable Development Goals (SDGs), particularly goals 7, 11, and 13. Full article

Environmental comfort and air pollution are among the most important indicators for assessing the population’s quality of life in urban agglomerations. This study aims to explore long-term memory in air pollution time series by analyzing the dynamics of the Hurst exponent and evaluating the predictability index. This type of statistical pre-forecast analysis is essential for developing accurate forecasting models for such time series. The effectiveness of air quality monitoring systems largely depends on the precision of these forecasts. The Ekibastuz coal-mining center, which houses one of the largest coal-fired power stations in Kazakhstan and the world, with a capacity of about 4000 MW, was chosen as an example for the study. Data for the period from 1 March 2023 to 31 December 2024 were collected and analyzed at the Ekibastuz coal-fired power station. During the specified period, 14 indicators (67,527 observations) were collected at 10 min intervals, including mass concentrations of CO, NO, NO₂, SO₂, PM_2.5, and PM₁₀, as well as current mass consumption of CO, NO, NO₂, SO₂, dust, and NO_x. The detrended fluctuation analysis of a time series of air pollution indicators was used to calculate the Hurst exponent and identify long-term memory. Changes in the Hurst exponent in regards to dynamics were also investigated, and a predictability index was calculated to monitor emissions of pollutants in the air. Long-term memory is recorded in the structure of all the time series of air pollution indicators. Dynamic analysis of the Hurst exponent confirmed persistent time series characteristics, with an average Hurst exponent of about 0.7. Identifying the time series plots for which the Hurst exponent is falling (analysis of the indicator of dynamics), along with the predictability index, is a sign of an increase in the influence of random factors on the time series. This is a sign of changes in the dynamics of the pollutant release concentrations and may indicate possible excess emissions that need to be controlled. Calculating the dynamic changes in the Hurst exponent for the emission time series made it possible to identify two distinct clusters corresponding to periods of persistence and randomness in the operation of the coal-fired power station. The study shows that evaluating the predictability index helps fine-tune the parameters of time series forecasting models, which is crucial for developing reliable air pollution monitoring systems. The results obtained in this study allow us to conclude that the method of trended fluctuation analysis can be the basis for creating an indicator of the level of air pollution, which allows us to quickly respond to possible deviations from the established standards. Environmental services can use the results to build reliable monitoring systems for air pollution from coal combustion emissions, especially near populated areas. Full article

The DNDC-Rice model effectively simulates yield and greenhouse gas emissions within a paddy system, while its performance under upland conditions remains unclear. Using data from a long-term cover crop experiment (fallow [FA] vs. rye [RY]) in a soybean field, this study validated the DNDC-Rice model’s performance in simulating soil dynamics, crop growth, and C-N cycling processes in upland systems through various indicators, including soil temperature, water-filled pore space (WFPS), soybean biomass and yield, CO₂ and N₂O fluxes, and soil organic carbon (SOC). Based on simulated results, the underestimation of cumulative N₂O flux (25.6% in FA and 5.1% in RY) was attributed to both underestimated WFPS and the algorithm’s limitations in simulating N₂O emission pulses. Overestimated soybean growth increased respiration, leading to the overestimation of CO₂ flux. Although the model captured trends in SOC stock, the simulated annual values differed from observations (−9.9% to +10.1%), potentially due to sampling errors. These findings indicate that the DNDC-Rice model requires improvements in its N cycling algorithm and crop growth sub-models to improve predictions for upland systems. This study provides validation evidence for applying DNDC-Rice to upland systems and offers direction for improving model simulation in paddy-upland rotation systems, thereby enhancing its applicability in such contexts. Full article