• Energy Research
• 13. Climate action close
• 11. Sustainability close
• 7. Clean energy close
• Frontiers in Environmental Science close

Interconnected food, energy, and water (FEW) nexus systems face many challenges to support human well-being (HWB) and maintain resilience, especially in arid and semiarid regions like New Mexico (NM), United States (US). Insufficient FEW resources, unstable economic growth due to fluctuations in prices of crude oil and natural gas, inequitable education and employment, and climate change are some of these challenges. Enhancing the resilience of such coupled socio-environmental systems depends on the efficient use of resources, improved understanding of the interlinkages across FEW system components, and adopting adaptable alternative management strategies. The goal of this study was to develop a framework that can be used to enhance the resilience of these systems. An integrated food, energy, water, well-being, and resilience (FEW-WISE) framework was developed and introduced in this study. This framework consists mainly of five steps to qualitatively and quantitatively assess FEW system relationships, identify important external drivers, integrate FEW systems using system dynamics models, develop FEW and HWB performance indices, and develop a resilience monitoring criterion using a threshold-based approach that integrates these indices. The FEW-WISE framework can be used to evaluate and predict the dynamic behavior of FEW systems in response to environmental and socioeconomic changes using resilience indicators. In conclusion, the derived resilience index can be used to inform the decision-making processes to guide the development of alternative scenario-based management strategies to enhance the resilience of ecological and socioeconomic well-being of vulnerable regions like NM.

The objective of this paper is to analyse impacts of COVID-19 on the nexus of food systems, the environment and sustainable development and propose ways for governments and international agencies to mitigate impacts in the short and medium term. It covers the historic period from early 2020 to early 2021 and also makes an assessment on future prospects. Although evidence is collected from all around the world, the focus is primarily on developing countries. The methods used are a review of the announced actions and preliminary findings in the academic and grey literature as well as on reliable websites from global and international institutions. By October 2020, governments around the world had invested about $12 trillion to counteract the economic effects of COVID-19. This investment could contribute to progress on the SDGs and global climate targets insofar as it was invested within a framework that supports both socio-economic recovery and sustainability. Initial analysis indicates that investments for economic recovery did not sufficiently address food security and sustainability, concentrating instead on immediate economic risk management. The global sustainable development agenda must promote the resilience and sustainability of food systems through policies and measures that: i) account for environmental thresholds and trade-offs; ii) promote food security and healthy diets; iii) enhance and protect rural livelihoods; and iv) address the inequalities and injustices that have emerged and will prevail during a post-COVID transition. National stimulus programs and the actions of international agencies must be assessed and monitored to deliver multiple benefits simultaneously and guide building back better.

The topic of whether globalization, energy consumption and financial development can substantially reduce emissions during the globalization era remains unanswered. In this context, this research highlights empirical indications supporting this theoretical discord; assessing the effect of globalization, energy consumption and financial development on the CO2 emissions in Japan (utilizing a dataset that spans between 1990 and 2019). The study employed the Autoregressive Distributed Lag (ARDL) technique and frequency domain causality to probe these relationships. Unlike other conventional causality tests, the frequency domain causality test can capture causality at different frequencies. The findings from the ARDL analysis disclosed that globalization and renewable energy contribute to the mitigation of CO2 emissions while fossil fuel, economic growth and financial development caused an upsurge in CO2 in Japan. Furthermore, the frequency domain demonstrated that all the exogenous variables can forecast CO2 mostly in the long-term which implies that any policy initiated based on the exogenous variables will impact emissions of CO2. Based on the results obtained, Japan has to improve its financial systems and increase its use of renewable energy. Furthermore, Japan needs to restructure its policy regarding globalization owing to the fact that it contributes to the degradation of the environment. Since globalization is a major driver of economic growth, the government should concentrate on luring and licensing investors that use environmentally beneficial (net-zero) technology.

Population and industry are closely related to CO2 emissions in Cities. However, few studies have explored the joint influence of population size and industrial structure on CO2 emissions. This paper examined the nonlinear influence of population size and industrial structure on CO2 emissions by using a threshold-STIRPAT model with the latest available data in 2001–2017 from 255 cities in China. Results indicated that the promotion effect of urban population size on CO2 emissions increased in the first two stages and then decreased in the third stage when the industrial structure exceeded the threshold value of 1.22. Meanwhile, the industrial structure had a positive impact on CO2 emissions if the urban population was less than 1.38 million. However, the previous promotional effect became an inhibitory effect when the urban population exceeded 1.38 million. According to the above findings, it is necessary to find a reasonable match between urban population size and industrial structure. Specifically, China should formulate differentiated urban population policies in cities with different industrial structures. In addition, for cities with a population size of more than 1.38 million, adjusting the industrial structure to give priority to the tertiary industry will be an effective way to reduce CO2 emissions.

Regional carbon emissions related to forest cover change (FCC) and wood harvest (WH) are critical for the accurate estimates of global carbon balance over an extended time period. Using remote sensing and inventory data, this study provides a comprehensive record of FCC, WH, and their integrated carbon emissions between 1908 and 2015 in the dry temperate regions of Pakistan. Results demonstrate a significant decline in forest area (21,034 ha) at an annual rate of 0.56% from 1973 to 2015. The total WH was 24.84 million m3 (0.23 million m3 yr−1) between 1908 and 2015. Deforestation was responsible for a net loss of 1.39 million Mg C (0.018 million Mg C yr−1), while WH-related carbon emissions accounted for 11.29 million Mg C (0.52 million Mg C yr−1). The present results indicate that under the existing FCC and WH harvest scenario, the forests are acting as a net source of 0.29 million Mg C yr−1. Agriculture expansion and the heavy dependency of local communities on the forest’s resources, exclusion of conservation and local communities from forest management, insufficient monitoring, and weak law-enforcement were the striking drivers of FCC, WH, and their related emissions. These findings suggest that to maintain forest carbon and meet the communities’ requirements, counter approaches such as agriculture incentives, agroforestry, trophy hunting, alternative energy sources, and inclusion of conservation and secure community-based management are needed.

Integrated assessment models (IAMs) capture synergies between human development and natural ecosystems that have important implications for the food-energy-water (FEW) nexus. However, their lack of fine-scale representation of water regulatory structure and landscape heterogeneity impedes their application to FEW impact studies in water-limited basins. To address this limitation, we developed a framework for studying effects of global change on regional outcomes for food crops, bioenergy, hydropower, and instream flows. We applied the new methodology to the Columbia River Basin (CRB) as a case study. The framework uses the Demeter land-use and land-cover change (LULCC) downscaling tool, which we updated so that water rights are spatially integrated in the land allocation process. We downscaled two LULCC scenarios (SSP2-RCP 4.5 and SSP5-RCP 8.5) under three levels of irrigation expansion: no expansion (historical extent), moderate expansion (all land presently authorized by a water right is irrigated), and maximum expansion (new water rights are granted to cover all irrigable land). The downscaled scenarios were evaluated using a hydrology-cropping systems model and a reservoir model coupled in a linear fashion to quantify changes in food and bioenergy crop production, hydropower generation, and availability of instream flows for fish. The net changes in each sector were partitioned among climate, land use, and irrigation-expansion effects. We found that climate change alone resulted in approximately 50% greater production of switchgrass for bioenergy and 20% greater instream flow deficits. In the irrigation-expansion scenarios, the combination of climate change and greater irrigated extent increased switchgrass production by 76% to 256% at the cost of 42% to 165% greater instream flow deficits and 0% to 8% less hydropower generation. Therefore, while irrigation expansion increased bioenergy crop productivity, it also exacerbated seasonal water shortages, especially for instream use. This paper provides a general framework for assessing benchmark scenarios of global LULCC in terms of their regional FEW subsystem outcomes.

Air temperature is the primary indicator of climate change. Reanalysis temperature products are important datasets for temperature estimates over high-elevation areas with few meteorological stations. However, they contain biases in observations, so a bias correction is required to enhance the accuracy of modeling predictions. In this study, we used the temperature lapse-rate method to correct ERA-Interim reanalysis-temperature data in the Qilian Mountains of China from 1979 to 2017. These temperature lapse rates were based on observations (ΓObs) and on model internal vertical lapse rates derived from different ERA-Interim pressure levels (ΓERA). The results showed that the temperature lapse rates in warm periods were larger than those in cold periods. Both the original and corrected ERA-Interim temperature can significantly capture the warming trend exhibited by observations. In general, the temperature lapse rate method was reliable for correcting ERA-interim reanalysis-temperature data. Although ΓObs performed best in bias correction, it depends heavily on the density of ground observation stations and is not appropriate for remote areas with a low data coverage. Correction methods based on ΓERA were shown to be reliable for bias correction, and will be especially applicable to mountainous areas with few observation stations. Our results contribute to the improvement of quality of data products and enhance the accuracy of modeling of climate change effects and risks to the environment and human health.

Over the first two decades of the 21st century, many wetlands in eastern Australia exhibited declining water levels, causing concern for communities and environmental managers and raising questions about the roles of climate change and other human activity in these water level declines. In this context we examine the causes of water level variability in four wetlands on North Stradbroke Island (Minjerribah), in the humid subtropics of south-eastern Queensland, Australia, using a combination of hydrological and water isotope monitoring and modelling. North Stradbroke Island has a high concentration of wetlands perched above the regional water table, with cultural and ecological significance, and value for palaeoclimate research. From 2015 to 2019, wetland water depths decreased markedly at all sites, coinciding with increases in oxygen isotope ratios in surface waters. The data indicate that climate, specifically a decrease in precipitation relative to evaporation, was responsible for those declining water levels, and that groundwater extraction did not play a critical role. At two of the sites—both palustrine wetlands—declining surface water levels led to intermittent connectivity with the local perched aquifers. At the other two sites, which are both shallow lakes, the surface waters were constantly fed by perched groundwater. The hydrology of the two lakes was modelled using simple mass balance. However, in order to accurately model lake level change, it was necessary to vary catchment runoff and lake outflow via groundwater through time, highlighting complexity in projecting future hydrological change in these lakes. The long term resilience of these lakes depends on a combination of rainfall regime and the balance between catchment runoff and groundwater throughflow, the future of which is highly uncertain. As a consequence, continued efforts to project future hydroclimate and to model the complex hydrology of subtropical wetlands are essential.