To address the multifaceted challenge of sustainability posed by human interventions and climate change, the urgent need to harness agroforestry for biomass production, carbon sequestration, and the integration of the water-food-energy nexus has been recognized. This approach has not only provided innovative solutions but also highlighted the complexities and difficulties inherent in achieving sustainable development. This systematic literature review provides a comprehensive overview of research spanning 24 years, elucidating the role of agroforestry in mitigating climate change impacts, enhancing biomass provision, carbon sequestration, and optimizing the water-food-energy nexus. Various forms of agroforestry systems exhibit differing capacities to supply biomass and sequester carbon. In a study of a poplar-based agroforestry system, the dry biomass yield of poplar ranged from 69.90 to 207.98 Mg ha⁻¹ aboveground and 13.46 to 36.69 Mg ha⁻¹ belowground across five different planting geometries. The total carbon storage, encompassing both above and belowground biomass, varied among spacing configurations, with values of 112.48, 101.80, 84.87, 77.28, and 38.84 Mg C ha⁻¹, respectively. Further, agroforestry (sapota-cowpea-castor) decreased soil loss and runoff by 37.7 and 19.1%, respectively, compared to sole crop cultivation. Similarly in another study, the Karanda (Carissa sp.) based agroforestry system with a mung bean-potato system achieved the highest net return (3529.1 US$ ha⁻¹) and water use efficiency (33.0 kg ha-mm⁻¹). The review synthesizes findings from diverse studies highlighting the multifunctional benefits of agroforestry systems across various geographical regions and agroecological contexts. Key themes explored include biomass production, carbon sequestration potential, and the intricate linkages between water, food, and energy security within agroforestry landscapes. Through a synthesis of empirical evidence, the review underscores the capacity of agroforestry to enhance ecosystem resilience, mitigate greenhouse gas emissions, and foster sustainable livelihoods for rural communities. Moreover, it examines the synergies and trade-offs inherent in agroforestry interventions, considering factors such as species selection, management practices, and socio-economic considerations. The review also identifies gaps in current knowledge and areas requiring further research attention, such as the scaling up of agroforestry practices, socio-economic impacts on local communities, and policy frameworks for mainstreaming agroforestry into national and international climate and development agendas. Overall, this review underscores the pivotal role of agroforestry as a holistic approach to achieving multiple SDGs, particularly in the face of climate change. By integrating biomass provision, carbon sequestration, and optimizing resource use through the water-food-energy nexus, agroforestry offers a sustainable pathway toward resilient and equitable development.

As countries worldwide strive for a sustainable energy transition, the role of natural gas in achieving carbon neutrality targets has gained increasing attention. This study evaluates the energy efficiency of natural gas (EENG) in China from 2008 to 2021, using the Slacks-Based Measure Data Envelopment Analysis model to examine temporal dynamics and regional disparities. The spatial Durbin model is employed to investigate the factors influencing EENG, considering spatial spillover effects. The results reveal an overall improvement in China’s EENG over time, with significant temporal and regional variations. Urbanization, environmental regulation, natural gas infrastructure, industrial structure, and technological innovation are found to have significant positive impacts on EENG, with notable spatial spillover effects and regional heterogeneity. These findings contribute to the literature on sustainable energy transitions and provide valuable insights for policymakers to develop targeted strategies for enhancing natural gas efficiency and achieving carbon neutrality in China.

The utopia-tracking method, used to find compromise solutions or trade-offs in multi-objective problems, is proposed as a tool to assign economic and environmental values to user behavior. To this end, an optimal design model of an isolated energy supply system is proposed that selects, using continuous variables, different technologies to integrate a photovoltaic system. The nonlinear programming model computes the size of the system, including the storage unit. The design is approached using a base demand, which corresponds to the real data obtained from the case study, and subsequently the optimal user behavior is calculated to reduce the total annual cost of the system and the equivalent emissions, obtaining a demand coupled to the operation and optimal system design. The relevance of penalties such as the carbon tax on renewable systems is evaluated. The results indicate that the use of carbon penalties does not have a significant effect on emissions control and that, by modifying user behavior, reductions of 8 % in the system cost and just over one ton of CO$_2 eq$ per year in emissions. Finally, the calculation of compromise solutions is presented as more effective ways to reduce emissions than the use of emissions monetization.

Integrating renewable energy sources (RESs) with traditional thermal power systems has become an essential economic and environmental imperative. The optimal power flow (OPF) problem ensures optimal power system performance while meeting various constraints. The static OPF problem focuses on the minimization of the objective functions for a single hour. However, the multi-period OPF (MOPF) problem involves optimizing the power system for the different time intervals for the 24 hour time horizon. Optimizing a RESs-integrated power system involves several uncertain and complex operating states that can be handled by a robust evolutionary optimization algorithm (EOA). This article focuses on the development of an enhanced performance-based differential search algorithm for obtaining a high-quality, accurate, and stable solution for the hybrid static OPF as well as MOPF model, including conventional thermal generators as well as multiple intermittent RESs: wind energy, photovoltaic energy, tidal energy, small hydro system, plug-in electric vehicle, and battery energy storage system. A probabilistic approach based on an interpretable mathematical technique is used to model the uncertainties of the RESs. Three sophisticated alteration techniques are dynamic population reduction schemes for efficient exploration of the search space in the earlier iteration steps while exploiting the available solution sets in the latter iteration steps in an effective manner, quasi-opposition-based learning for an effective search space exploration, a best artificial-organism-guided stopoversite discovering technique, to accelerate the local searchability by improving the exploitation capability. Simulation experiments are performed on modified IEEE 30 and 118-bus systems to evaluate the proposed method’s performance, and the results are compared to the eight sophisticated EOAs. The result analysis demonstrates that our proposed EOA outperforms the considered EOAs in terms of solution accuracy, quality, and convergence ability for addressing the hybrid static OPF and MOPF problems.

Air pollution causes around seven million deaths annually worldwide, yet research on the combined impacts of urban pollutants is limited, hindering effective mitigation strategies. This is a concern in Kigali and the East Africa region, where a notable lack of detailed long-term data, limited studies on their health impacts, and a lack of comprehensive methods for assessing urban air pollution impacts and environmental-health risks. This study addresses these gaps, specifically targeting personal exposure to PM₁₀ and NO₂, monitored across six stations in Kigali throughout 2021. These pollutants were selected due to their long-term data availability and significant health-impacts. Utilizing computational analysis of the urban air quality method, we identified vulnerable zones, quality indicators, and impacts and risks associated with urban air pollution. Results reveal that PM₁₀ levels (42.0–56.0 µg m⁻³ and NO₂ levels (15.5–20.4 µg m⁻³) have quality exceeding WHO-2021 guidelines (PM₁₀: 15 µg m⁻³ and NO₂: 10 µg m⁻³) indicating severe air quality issues in Kigali. The variation between monitoring stations was statistically significant (p<0.05), indicating notable spatial differences within the study area. The probability of exposure by zones (0.14–0.3) is linked to traffic and household emissions. The identified high-impact for PM₁₀ suggests more significant concerns and the need for mitigation, while the low-risk for both pollutants indicates a relatively low immediate health threat. These findings highlight the need for strategic mitigation measures and targeted air quality management policies to control traffic and household emissions, which are essential for improving Kigali’s air quality and safeguarding public health.

The effect of the low-carbon pilot policy exhibits significant heterogeneity.

Provinces implement both low-carbon pilot policy and CETS have better effects.

Carbon reduction measures in five pilot provinces to overcome the uncertainty.