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The influence of climate change on the regional hydrological cycle has been an international scientific issue that has attracted more attention in recent decades due to its huge effects on drought and flood. It is essential to investigate the change of regional hydrological characteristics in the context of global warming for developing flood mitigation and water utilization strategies in the future. The purpose of this study is to carry out a comprehensive analysis of changes in future runoff and flood for the upper Huai River basin by combining future climate scenarios, hydrological model, and flood frequency analysis. The daily bias correction (DBC) statistical downscaling method is used to downscale the global climate model (GCM) outputs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and to generate future daily temperature and precipitation series. The Xinanjiang (XAJ) hydrological model is driven to project changes in future seasonal runoff under SSP245 and SSP585 scenarios for two future periods: 2050s (2031–2060) and 2080s (2071–2100) based on model calibration and validation. Finally, the peaks over threshold (POT) method and generalized Pareto (GP) distribution are combined to evaluate the changes of flood frequency for the upper Huai River basin. The results show that 1) GCMs project that there has been an insignificant increasing trend in future precipitation series, while an obvious increasing trend is detected in future temperature series; 2) average monthly runoffs in low-flow season have seen decreasing trends under SSP245 and SSP585 scenarios during the 2050s, while there has been an obvious increasing trend of average monthly runoff in high-flow season during the 2080s; 3) there is a decreasing trend in design floods below the 50-year return period under two future scenarios during the 2050s, while there has been an significant increasing trend in design flood during the 2080s in most cases and the amplitude of increase becomes larger for a larger return period. The study suggests that future flood will probably occur more frequently and an urgent need to develop appropriate adaptation measures to increase social resilience to warming climate over the upper Huai River basin.

The global community has set intensive targets in Sustainable Development Goals (SDGs) to better people’s lives after closing the Millennium Development Goals (MDGs). It corresponds to the 2030 aspirations of the United Nations to enhance and promote the sustainable development of human society. The current paper explores the impact of fiscal hedging and R&D in energy Using a green-energy system in SDGs. To do this, we used TOPSIS and QARDL methodologies on a 21-year dataset of South and Southeast Asian economies from 2000 to 2020. The study results show that fiscal hedging contributes favourably to the environmental degradation of the underlying economy. Research and development (R&D) in renewables has contributed negatively to ecological degradation and SDGs in the economies of South & Southeast Asia. This study suggests policy guidelines for advanced and developing economies based on fiscal stability and technical innovation through R&D to meet SDG.

The management of municipal solid waste (MSW) is a complex and expensive task. This is especially the case in developing countries, where waste generation rates are continuously increasing and where current MSW management strategies are focused on inadequate practices, such as landfilling and incineration, which result in numerous health and environmental problems. The anaerobic digestion (AD) of MSW has been implemented worldwide as a solution to decrease the amount of waste ending up in landfills. This process allows for the recovery of energy from the organic fraction of MSW (OFMSW) in the form of biogas, which is largely composed of methane. Therefore, the goal of the present study was to evaluate the biochemical methane potential (BMP) of the OFMSW generated within different socioeconomic strata of the Metropolitan Area of Guadalajara (MAG), Mexico. From a microscale perspective, the microbial communities within the experimental AD system were analyzed using high-throughput sequencing of the 16S rRNA gene to assess the relationship between these communities and the biogas composition. This microbial identification revealed a typical AD composition consisting of the following six phyla: Actinobacteria, Bacteroidetes, Chloroflexi, Euryarchaeota, Firmicutes, and Proteobacteria. Furthermore, through the identification of Methanobacterium and Methanosaeta, two methanogenesis pathways (hydrogenotrophic and acetoclastic) were pinpointed. From a macroscale perspective, a multi-stage Gompertz kinetic model was used to describe cumulative biogas production. This model considered the complex nature of the OFMSW substrate in order to estimate the potential level of biogas production in the MAG using a weighted average that was based on the size of the population in each socioeconomic stratum evaluated (732.8 mL⋅g−1 VS). This novel contribution to the literature provides an estimation of the potential economic, energetic, and environmental benefits of treating the OFMSW produced in the MAG through AD. Through this approach, an estimated 8.5 MWh·year−1 of electrical power could be produced, translating into 1.13 million USD of yearly revenue and resulting in reduced GHG emissions (10,519 tonne CO2eq⋅year−1).

The accuracy of carbon emission data is essential for various disciplines to maintain reasonable expectations and certainty regarding the carbon emission rights trading market. However, the management of carbon emission data quality faces many challenges, especially due to the harmful behavior of enterprises that falsify such data, which seriously disrupts the order and credibility of the carbon market. Currently, few studies focus on the behavior and mechanism of corporate carbon emission data fraud, which requires an in-depth stakeholder analysis to obtain theoretical and empirical support for the formulation of effective regulatory policies. To investigate the influence of government regulation and media monitoring on addressing enterprise carbon emission data falsification, as well as to analyze the game behaviors and equilibrium outcomes among the government, media, and enterprises under different policy combinations and market environments, this study develops an evolutionary game model incorporating the government, media, and enterprises as three key stakeholders. Furthermore, numerical simulations are conducted for empirical validation. The key findings of this research highlight the significant impact of government regulation and media monitoring on deterring enterprise carbon emission data falsification, thus effectively reducing falsification motives and behaviors and enhancing the quality of carbon emission data. Additionally, the game between the government, media, and enterprises reveals the existence of multiple evolutionary stable strategies. Of these, the optimal strategy is the comprehensive implementation of all three elements: government regulation, media monitoring, and corporate integrity disclosure. This paper comprehensively examines the influence of government regulation and media monitoring on enterprise carbon emission data falsification and addresses the gaps in existing research. Moreover, it provides theoretical guidance and policy recommendations for establishing a high-quality carbon market.

The global economy is undergoing a transformative shift propelled by continuous technological advancements. This digital revolution has ushered in a new era characterized by the pervasive influence of the digital economy. Notably, the inherent “green” attributes of the digital economy, such as reduced marginal costs and diminished environmental impact, have injected fresh momentum into the green transformation of the industrial sector. Using spatial econometric model, we examine the impact of the digital economy on the green transformation of the manufacturing industry using panel data for 283 prefecture-level Chinese cities from 2011 to 2019. We first calculate the level of the manufacturing industry’s green transformation in this paper according to the Slack-Based Measure model. The green transformation of the industrial sector is facilitated by the digital economy in both the eastern and central regions, as revealed by heterogeneity analysis based on geographical areas. The moderating effect analysis reveals a distinct negative moderating impact of industrial structure upgrading and industrial agglomeration. Additionally, the threshold effect tests indicate significant nonlinear features in the influence of industrial structure upgrading on the green transformation of the manufacturing industry.

The contribution of artificial reservoirs to greenhouse gas (GHG) emissions has been emphasized in previous studies. In the present study, we collected and updated data on GHG emission rates from reservoirs at the global scale, and applied a new classification method based on the hydrobelt concept. Our results showed that CH4 and CO2 emissions were significantly different in the hydrobelt groups (p < 0.01), while no significant difference was found for N2O emissions, possibly due to their limited measurements. We found that annual GHG emissions (calculated as C or N) from global reservoirs amounted to 12.9 Tg CH4-C, 50.8 Tg CO2-C, and 0.04 Tg N2O-N. Furthermore, GHG emissions (calculated as CO2 equivalents) were also estimated for the 1950–2017 period based on the cumulative number and surface area of global reservoirs in the different hydrobelts. The highest increase rate in both the number of reservoirs and their surface area, which occurred from 1950 to the 1980s, led to an increase in GHG emissions from reservoirs. Since then, the increase rate of reservoir construction, and hence GHG emissions, has slowed down. Moreover, we also examined the potential impact of reservoir eutrophication on GHG emissions and found that GHG emissions from reservoirs could increase by 40% under conditions in which total phosphorus would double. In addition, we showed that the characteristics of reservoirs (e.g., geographical location) and their catchments (e.g., surrounding terrestrial net primary production, and precipitation) may influence GHG emissions. Overall, a major finding of our study was to provide an estimate of the impact of large reservoirs during the 1950–2017 period, in terms of GHG emissions. This should help anticipate future GHG emissions from reservoirs considering all reservoirs being planned worldwide. Besides using the classification per hydrobelt and thus reconnecting reservoirs to their watersheds, our study further emphasized the efforts to be made regarding the measurement of GHG emissions in some hydrobelts and in considering the growing number of reservoirs.

Moving away from fossil fuels is essential for a sustainable future. Carrying out this transition without reversing the improvements in the quality of life is the ultimate challenge. While minimizing the anticipated impacts of climate change is the primary driver of decarbonization, the inevitable exhaustion of fossil energy sources should provide just as strong or perhaps even stronger incentives. The vast majority of publications outlining the pathways to “net-zero carbon emission” fall short from leading to a truly “fossil fuel-free” future without falling back to some level of dependence on fossil fuels with carbon capture and sequestration. While carbon capture and sequestration might be a necessary step toward decarbonization, such intermediate goals might turn into a dead end without defining the end point. The main obstacle to wider adoption of renewable energy resources is their inherent intermittency. Solar and wind are, by far, the most abundant renewable energy sources that are expected to take the lion share in transitioning to a sustainable future. Intermittency arises at multiple levels. The most recognized are the short-term (minute-by-minute, hourly, or diurnal) variations that should be the easiest to address. Less frequently realized are the seasonal and inter-annual variabilities. Seasonality poses far greater challenges than minute-by-minute or hourly variations because they lead to the absence of energy resources for prolonged periods of time. Our interest is the feasibility of a future where all energy (100%) comes from renewable sources leaving no room for fossil fuels. We carry out rudimentary statistical analyses of solar radiation and wind speed time series records to quantify the degree of their intermittencies seasonally and inter-annually. We employ a simple but robust accounting of the shortfalls when the supplies do not meet demand via a modified cumulative supply/deficit analysis that incorporates energy losses arising from transporting excess energy to storage and retrieving it as needed. The presented analysis provides guidance for choosing between the installation of excess capacity or the deployment of energy storage to guarantee reliable energy services under the assumption that the energy system is powered exclusively by renewable energy sources. This paper examines the seasonal and inter-annual variability of hydropower and biofuel resources to estimate their potential to mitigate the intermittencies of solar and wind resources. The presented analyses are meant to provide crude, bulk part estimates and are not intended for planning or operational purposes of the actual energy infrastructures. The primary focus of this paper is the Northeast region of the United States using the conterminous United States as a reference to assess the viability of reducing the energy storage need in the study region via improved connectivity to the national grid. This paper builds on the modeling exercises carried out as part of the climate-induced extremes on food, energy, water systems studies.

There is no consensus on whether state-owned enterprises (SOEs) or privately-owned enterprises (POEs) pollute more. This study explores the impact of ownership on pollution emission intensity using micro-data from Chinese industrial enterprises. From the perspective of energy efficiency, the mechanism of ownership affecting pollution emissions is explored further. Research results show that the pollution emission intensity of SOEs is significantly higher than that of POEs. The underlying reason is the low energy efficiency of SOEs, and energy efficiency plays an important mediating role in the relationship between ownership and pollution emissions. Among industrial waste gas, nitrogen oxides (NOx) and dust emissions, energy efficiency plays the largest mediating effect between ownership and NOx emissions. Additionally, in both high-polluting and low-polluting industries, SOEs’ pollutant emission intensity is higher than that of POEs, however, the mediation effect of energy efficiency is greater in low-polluting industries. In cities with high growth pressure, SOEs’ pollutant emission intensity is more significant than that of POEs. On the contrary, there are no noticeable differences in pollutant emission intensity between SOEs and POEs in cities with low growth pressure. But the mediation effect of energy efficiency is more significant in cities with high growth pressure. Industrial enterprises are the ultimate sources of industrial pollution. Therefore, the formulation of effective environmental policies cannot be separated from the analysis of enterprises’ emission behaviors and the assessment of micro factors affecting emissions. The conclusions of this study provide a basis for developing countries to formulate environmental policies for industrial enterprises.