• Energy Research
• 2021-2025close
• 11. Sustainability close
• University of North Texas close

Quantification of the trade-offs among greenhouse gas (GHG) emissions, yield, and farmers’ incomes is essential for proposing economic and environmental nitrogen (N) management strategies for optimizing agricultural production. A four-year (2017–2020) field experiment (including four treatments: basic N fertilizer treatment (BF), suitable utilization of fertilization (SU), emission reduction treatment (ER), and high fertilization (HF)) was conducted on maize (Zea mays L.) in the North China Plain. The Life Cycle Assessment (LCA) method was used in this study to quantify the GHG emissions and farmers’ incomes during the whole maize production process. The total GHG emissions of BF, SU, ER, and HF treatments in the process of maize production are 10,755.2, 12,908.7, 11,950.1, and 14,274.5 kg CO2-eq ha−1, respectively, of which the direct emissions account for 84.8%, 76.8%, 74.9%, and 71.0%, respectively. Adding inhibitors significantly reduced direct GHG emissions, and the N2O and CO2 emissions from the maize fields in the ER treatment decreased by 30.0% and 7.9% compared to those in the SU treatment. Insignificant differences in yield were found between the SU and ER treatments, indicating that adding fertilizer inhibitors did not affect farmers’ incomes while reducing GHG emissions. The yield for SU, ER, and HF treatments all significantly increased by 12.9–24.0%, 10.0–20.7%, and 2.1–17.4% compared to BF, respectively. In comparison with BF, both SU and ER significantly promoted agricultural net profit (ANP) by 16.6% and 12.2%, with mean ANP values of 3101.0 USD ha−1 and 2980.0 USD ha−1, respectively. Due to the high agricultural inputs, the ANP values in the HF treatment were 11.2%, 16.6%, and 12.4% lower than those in the SU treatment in 2018–2020. In conclusion, the combination of N fertilizer and inhibitors proved to be an environmentally friendly, high-profit, and low-emissions production technology while sustaining or even increasing maize yields in the North China Plain, which was conducive to achieving agricultural sustainability.

The complex interaction between social, economic, and environmental processes coupled with transformations of the landscape primarily driven by urbanization have impacts on the access, availability, and distribution, of food. This has resulted in a global micronutrient deficiency and hunger. Given rapid urbanization and population growth, a more sustainable food system is necessary to feed more urban populations and provide adequate nutrition, especially in developing countries. Existing frameworks for modelling urban-environment interactions contain components related to food security, however, lack the specificity needed to evaluate the effects of land use decisions and agricultural production strategies on the health of local populations measured through metrics such as nutritional output. The research presented here proposes an urban nutrition (UN) extension to the previously published urban ecological economic system by developing a focused component that simulates scenarios of different degrees of urbanization and agricultural production techniques to improve the nutritional output of agricultural land, while considering the conservation of soil. This simulation approach was subsequently applied to the Toluca Metropolitan Zone, Mexico. Results showed that nutritional output would greatly increase when adding a variety of crops, even in scenarios where agricultural land is limited. The proposed extension can be used by decision makers worldwide to evaluate how landscape configurations and agricultural production systems affect the nutritional needs of the local population while fostering sustainable practices.

AbstractThe role of climate change on global malaria is often highlighted in World Health Organisation reports. We modelled a Zambian socio-environmental dataset from 2000 to 2016, against malaria trends and investigated the relationship of near-term environmental change with malaria incidence using Bayesian spatio-temporal, and negative binomial mixed regression models. We introduced the diurnal temperature range (DTR) as an alternative environmental measure to the widely used mean temperature. We found substantial sub-national near-term variations and significant associations with malaria incidence-trends. Significant spatio-temporal shifts in DTR/environmental predictors influenced malaria incidence-rates, even in areas with declining trends. We highlight the impact of seasonally sensitive DTR, especially in the first two quarters of the year and demonstrate how substantial investment in intervention programmes is negatively impacted by near-term climate change, most notably since 2010. We argue for targeted seasonally-sensitive malaria chemoprevention programmes.

China, with its growing population and economic development, faces increasing risks to health from climate change, but also opportunities to address these risks and protect health for generations to come. Without a timely and adequate response, climate change will impact lives and livelihoods at an accelerated rate. In 2020, the Lancet Countdown Regional Centre in Asia, led by Tsinghua University, built on the work of the global Lancet Countdown and began its assessment of the health profile of climate change in China with the aim of triggering rapid and health-responsive actions. This 2021 report is the first annual update, presenting 25 indicators within five domains: climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement. The report represents the contributions of 88 experts from 25 leading institutions in, and outside of, China. From 2020 to 2021, five new indicators have been added and methods have been improved for many indicators. Where possible, the indicator results are presented at national and provincial levels to facilitate local understanding and policy making. In a year marked by COVID-19, this report also endeavours to reflect on China's pathway for a green recovery, ensuring it aligns with the carbon neutrality goal, for the health of the current and future generations.

AbstractPlanetary health provides a perspective of ecological interdependence that connects the health and vitality of individuals, communities, and Earth's natural systems. It includes the social, political, and economic ecosystems that influence both individuals and whole societies. In an era of interconnected grand challenges threatening health of all systems at all scales, planetary health provides a framework for cross‐sectoral collaboration and unified systems approaches to solutions. The field of allergy is at the forefront of these efforts. Allergic conditions are a sentinel measure of environmental impact on human health in early life—illuminating how ecological changes affect immune development and predispose to a wider range of inflammatory noncommunicable diseases (NCDs). This shows how adverse macroscale ecology in the Anthropocene penetrates to the molecular level of personal and microscale ecology, including the microbial systems at the foundations of all ecosystems. It provides the basis for more integrated efforts to address widespread environmental degradation and adverse effects of maladaptive urbanization, food systems, lifestyle behaviors, and socioeconomic disadvantage. Nature‐based solutions and efforts to improve nature‐relatedness are crucial for restoring symbiosis, balance, and mutualism in every sense, recognizing that both personal lifestyle choices and collective structural actions are needed in tandem. Ultimately, meaningful ecological approaches will depend on placing greater emphasis on psychological and cultural dimensions such as mindfulness, values, and moral wisdom to ensure a sustainable and resilient future.

Considering urbanization can lead to irreversible land transformations, it is crucial to provide city managers, environmental resources managers, and even people with accurate predicting land use/land cover (LULC) to accomplish sustainable development goals. Although many methods have been used to predict land use/land cover (LULC), few studies have compared them. Therefore, by analyzing the results of various prediction models and, consequently, recognizing the most accurate and reliable ones, we can assist city managers, environmental resources managers, and researchers.. In this regard, this research compares Cellular Automata–Markov Chain and Artificial Neural Network (ANN) as frequently used models to overcome this gap and help those concerned about sustainable development to predict urban sprawl with the most reliable accuracy. In the first step, Landsat satellite images acquired in 2000, 2010, and 2020 were classified with Maximum Likelihood Classification (MLC), and LULC maps were prepared for each year. In the second step, to investigate the LULC prediction, validation of the CA–Markov and ANN methods was performed. In this way, the LULC simulation map of 2020 was prepared based on the LULC map of 2000 and 2010; next, the predicted LULC map of 2020 and the actual LULC map for 2020 were compared using correctness, completeness, and quality indices. Finally, the LULC map for 2030 was generated using both algorithms, and the corresponding change map was extracted, showing a reduction in soil and vegetation areas (respectively, 39% and 12%) and an expansion (58%) in built-up regions. Moreover, the validation test of the methods showed that the two algorithms were closer to each other; however, ANN had the highest completeness (96.21%) and quality (93.8%), while CA–Markov had the most correctness (96.47%). This study showed that the CA–Markov algorithm is more accurate in predicting the future of larger areas with higher allocations (urban and vegetation cover) while the ANN algorithm is more accurate in predicting the future of small areas with fewer allocations (soil and rock).

Remnants of native tallgrass prairie experience elevated atmospheric nitrogen (N) deposition in urban areas, with potential effects on species traits that are important for N cycling and species composition. We quantified bulk (primarily wet) inorganic N (NH4+-N + NO3--N) deposition at six sites along an urban development gradient (6–64% urban) in the Dallas-Fort Worth metropolitan area from April 2014 to October 2015. In addition, we conducted a phytometer experiment with two common native prairie bunchgrass species––one well studied (Schizachyrium scoparium) and one little studied (Nasella leucotricha)––to investigate ambient N deposition effects on plant biomass and tissue quality. Bulk inorganic N deposition ranged from 6.1–9.9 kg ha-1 yr-1, peaked in spring, and did not vary consistently with proportion of urban land within 10 km of the sites. Total (wet + dry) inorganic N deposition estimated using bulk deposition measured in this study and modeled dry deposition was 12.9–18.2 kg ha-1 yr-1. Although the two plant species studied differ in photosynthetic pathway, biomass, and tissue N, they exhibited a maximum 2-3-fold and 2-4-fold increase in total biomass and total plant N, respectively, with 1.6-fold higher bulk N deposition. In addition, our findings indicate that while native prairie grasses may exhibit a positive biomass response to increased N deposition up to ~18 kg ha-1 yr-1, total inorganic N deposition is well above the estimated critical load for herbaceous plant species richness in the tallgrass prairie of the Great Plains ecoregion and thus may negatively affect these plant communities.

Almost 46% of the world's population resides in a rural landscape. Smart villages, alongside smart cities, are in need of time for future economic growth, improved agriculture, better health, and education. The smart village is a concept that improves the traditional rural aspects with the help of digital transformation. The smart village is built up using heterogeneous digital technologies pillared around the Internet-of-Thing (IoT). There exist many opportunities in research to design a low-cost, secure, and efficient technical ecosystem. This article identifies the key application areas, where the IoT can be applied in the smart village. The article also presents a comparative study of communication technology options. 5 Pages, Conference: IEEE 7th World Forum on Internet of Things (WF-IoT), New Orleans, June 2021