• Energy Research
• 2021-2025close
• Frontiers in Environmental Science close
• Energy Research close

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).

Heat waves (HW) are prolonged periods of excessively hot weather that can cause severe socioeconomic and environmental impacts. This study aims to evaluate the differences in stored heat and turbulent flux partitioning during a heat wave event in Mexico City, using observations from an eddy covariance tower during the period from 14 June to 21 June 2023. During this period, net radiation (Rn) and sensible heat flux (H) increased significantly, particularly from noon to evening, reflecting stable atmospheric conditions. The air temperature showed a noticeable increase in the afternoon and evening, whereas absolute humidity decreased. We found that during the heat wave, the Bowen ratio (β) increased by 80% during daylight hours and 65% over a full 24-hour period compared to the pre-heat wave period. This heat release at night prolonged warm conditions, intensifying heat stress. The partitioning of net radiation for latent heat (LE), sensible heat (H), and heat storage (ΔQs) showed significant changes; during the heat wave, 51% of Rn was allocated to H and 34% to ΔQs, compared to pre-heat wave values of 49% and 27%, respectively. This study introduces a new characterization of heat waves in terms of energy, emphasizing the significant shifts in energy flux partitioning and storage. The new characterization highlights the critical role of urban heat storage and its release in exacerbating heat stress during and after heat wave events. This approach provides a comprehensive understanding of the energy dynamics during heat waves, which is essential for developing effective mitigation strategies to combat the adverse effects of extreme heat in urban environments.

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.

Uncertainty can be an impediment to decision making and result in decision paralysis. In environmental flow management, system complexity and natural variability increase uncertainty. Climate change provides further uncertainty and can hinder decision making altogether. Environmental flow managers express reluctance to include climate change adaptation in planning due to large knowledge gaps in hydro-ecological relationships. We applied a hybrid method of hypothetical scenarios and closed ended questions within a survey to investigate ecological trade off decision making behaviours and cognitive processes of environmental flow managers. The scenarios provided were both similar to participants’ past experiences, and others were entirely unprecedented and hence unfamiliar. We found managers were more confident making decisions in situations they are familiar with, and most managers show low levels of confidence in making trade off decisions under uncertain circumstances. When given a choice, the most common response to uncertainty was to gather additional information, however information is often unavailable or inaccessible–either it does not exist, or uncertainties are so great that decisions are deferred. Given future rainfall is likely to be different from the past, environmental flow managers must work to adopt robust decision making frameworks that will increase confidence in decision making by acknowledging uncertainties. This can be done through tools developed to address decision making under deep uncertainty. Adapting these tools and methods to environmental flow management will ensure managers can begin to consider likely, necessary future trade-offs in a more informed, transparent and robust manner and increase confidence in decision making under uncertainty.

Taking holistic actions to improve urban air quality is central to reducing the health risks associated with urbanisation, yet local evidence-based and institutional frameworks to achieve this are still challenging especially in many low-and middle-income countries (LMICs). This paper develops and applies an integrated systemic approach to explore the state of air quality management in Nairobi, Kenya; as an LMIC exemplar city. The urban diagnostics approach developed assesses current particulate matter air pollution in Nairobi; quantifies anthropogenic emissions for the years 2015 and 2020 and projects scenarios of impacts of actions and inactions to 2030. This was combined with a review of grey literature on air quality policies, urban development and interviews with key stakeholders. The analysis suggests that commendable progress has been made to improve air quality in Nairobi but continuing hazardous levels of air pollution still require concerted policy efforts. Data available for numerical simulations have low spatial resolution and are generated from global emission inventories that can miss or misrepresent local emission sources. The current air quality data gap that needs to be addressed are highlighted. Strong political support is required to ensure that current air quality improvement approaches are evidence based to achieve long-term sustainability goals.