• Energy Research

Integrating the dynamics and interconnections of natural and human system properties into a single measure would make it simpler to reliably and repeatedly assess and compare different social-environmental systems (SES). We propose a novel metric to assess the magnitudes and variations in SES dynamics by integrating longitudinal gross domestic product, population, and ecosystem net primary production. We use annual public data across the Asian Drylands Belt (ADB) from 1992 through 2016 for 18 political entities as our testbed for assessing the efficacy of the metric. We perform cross-comparisons with existing natural and social science metrics to demonstrate the validity of the proposed metric, including the Human Development Index and the Palmer Drought Severity Index. The new metric demonstrates notable and meaningful differences in trends among the political entities that reflect major social, economic and environmental events over the 25-year period. It provides unique perspectives about the three pillar components (social, economic and environmental systems) in each of the 18 political entities (PE) of the ADB. The metric also shows meaningful associations with key economic and environmental indicators and great potential for broader application and evaluation, given additional testing in other countries, regions, and biomes.

The present study investigates the effects of different fertilization practices, including chemical and organic fertilizers, on CH4 and N2O emissions in various crop cultivation systems in Kazakhstan. The research focuses on three staple crops: wheat, barley, and corn, which are commonly grown in the region. A randomized complete block design field trial was conducted with three replications for each crop, totaling 27 plots. Gas sampling was carried out five times between June and September 2021, with cylindrical gas sampling chambers inserted into the soil at a depth of 10 cm. The concentrations of CH4 and N2O were analyzed using GS-MS. Results reveal that all three crops exhibited moderate to high CH4 and N2O emissions, with corn consistently displaying the highest emissions. Both chemical and organic fertilizers led to increased emissions of CH4 and N2O compared to control plots. The organic fertilizer treatment occasionally showed slightly higher emissions compared to chemical fertilizer treatment. However, the differences in CH4 and N2O concentrations between fertilized and unfertilized plots were not drastically significant. Notably, environmental factors, such as soil moisture and temperature, played a more prominent role in influencing CH4 and N2O production than the type of fertilizer applied. These findings underscore the significance of optimizing fertilization practices to minimize greenhouse gas emissions while maintaining crop productivity and promoting sustainable agriculture in Kazakhstan.

Lakes are important components for regulating carbon cycling within landscapes. Most lakes are regarded as CO2 sources to the atmosphere, except for a few eutrophic ones. Algal blooms are common phenomena in many eutrophic lakes and can cause many environmental stresses, yet their effects on the net exchange of CO2 (FCO2) at large spatial scales have not been adequately addressed. We integrated remote sensing and Eddy Covariance (EC) technologies to investigate the effects that algal blooms have on FCO2 in the western basin of Lake Erie—a large lake infamous for these blooms. Three years of long-term EC data (2012–2014) at two sites were analyzed. We found that at both sites: (1) daily FCO2 significantly correlated with daily temperature, light, and wind speed during the algal bloom periods; (2) monthly FCO2 was negatively correlated with chlorophyll-a concentration; and (3) the year with larger algal blooms was always associated with lower carbon emissions. We concluded that large algal blooms could reduce carbon emissions in the western basin of Lake Erie. However, considering the complexity of processes within large lakes, the weak relationship we found, and the potential uncertainties that remain in our estimations of FCO2 and chlorophyll-a, we argue that additional data and analyses are needed to validate our conclusion and examine the underlying regulatory mechanisms.

Abstract. In the global methane budget, the largest natural source is attributed to wetlands, which encompass all ecosystems composed of waterlogged or inundated ground, capable of methane production. Among them, northern peatlands that store large amounts of soil organic carbon have been functioning, since the end of the last glaciation period, as long-term sources of methane (CH4) and are one of the most significant methane sources among wetlands. To reduce uncertainty of quantifying methane flux in the global methane budget, it is of significance to understand the underlying processes for methane production and fluxes in northern peatlands. A methane model that features methane production and transport by plants, ebullition process and diffusion in soil, oxidation to CO2, and CH4 fluxes to the atmosphere has been embedded in the ORCHIDEE-PEAT land surface model that includes an explicit representation of northern peatlands. ORCHIDEE-PCH4 was calibrated and evaluated on 14 peatland sites distributed on both the Eurasian and American continents in the northern boreal and temperate regions. Data assimilation approaches were employed to optimized parameters at each site and at all sites simultaneously. Results show that methanogenesis is sensitive to temperature and substrate availability over the top 75 cm of soil depth. Methane emissions estimated using single site optimization (SSO) of model parameters are underestimated by 9 g CH4 m−2 yr−1 on average (i.e., 50 % higher than the site average of yearly methane emissions). While using the multi-site optimization (MSO), methane emissions are overestimated by 5 g CH4 m−2 yr−1 on average across all investigated sites (i.e., 37 % lower than the site average of yearly methane emissions).

To promote the development of Social Life Cycle Assessment (SLCA), we conducted a comprehensive review of recently developed frameworks, methods, and characterization models for impact assessment for future method developers and SLCA practitioners. Two previous reviews served as our foundations for this review. We updated the review by including a comprehensive list of recently-developed SLCA frameworks, methods and characterization models. While a brief discussion from goal, data, and indicator perspectives is provided in Sections 2 to 4 for different frameworks/methods, the focus of this review is Section 5 where discussion on characterization models for impact assessment of different methods is provided. The characterization models are categorized into two types following the UNEP/SETAC guidelines: type I models without impact pathways and type II models with impact pathways. Different from methods incorporating type I/II characterization models, another LCA modeling approach, Life Cycle Attribute Assessment (LCAA), is also discussed in this review. We concluded that methods incorporating either type I or type II models have limitations. For type I models, the challenge lies in the systematic identification of relevant stakeholders and materiality issues; while for type II models, identification of impact pathways that most closely and accurately represent the real-world causal relationships is the key. LCAA may avoid these problems, but the ultimate questions differ from those asked by the methods using type I and II models.

We identified four distinct clusters of 151 countries based on COVID-19 prevalence rate from 1 February 2020 to 29 May 2021 by performing nonparametric K-means cluster analysis (KmL). We forecasted future development of the clusters by using a nonlinear 3-parameter logistic (3PL) model, and found that peak points of development are the latest for Cluster I and earliest for Cluster IV. Based on partial least squares structural equation modeling (PLS-SEM) for the first twenty weeks after 1 February 2020, we found that the prevalence rate of COVID-19 has been significantly influenced by major elements of human systems. Better health infrastructure, more restriction of human mobility, higher urban population density, and less urban environmental degradation are associated with lower levels of prevalence rate (PR) of COVID-19. The most striking discovery of this study is that economic development hindered the control of COVID-19 spread among countries in the early stage of the pandemic. Highlights: While richer countries have advantages in health and other urban infrastructures that may alleviate the prevalence rate of COVID-19, the combination of high economic development level and low restriction on human mobility has led to faster spread of the virus in the first 20 weeks after 1 February 2020.