• Energy Research
• natural sciences close
• 11. Sustainability close
• 1. No poverty close
• 3. Good health close
• FI close
• PK close

Agricultural adaptation to climate change is critical for ensuring future food security. Social capital is important for climate change adaptation, but institutions and social networks at multiple scales (e.g., household, community, and institution) have been overlooked in studying agricultural climate change adaptation. We combine data from 13 sites in 11 low-income countries in East Africa, West Africa, and South Asia to explore how multiple scales of social capital relate to household food security outcomes among smallholder farmers. Using social network theory, we define three community organizational social network types (fragmented defined by lack of coordination, brokered defined as having a strong central actor, or shared defined by high coordination) and examine household social capital through group memberships. We find community and household social capital are positively related, with higher household group membership more likely in brokered and shared networks. Household group membership is associated with more than a 10% reduction in average months of food insecurity, an effect moderated by community social network type. In communities with fragmented and shared organizational networks, additional household group memberships is associated with consistent decreases in food insecurity, in some cases up to two months; whereas in brokered networks, reductions in food insecurity are only associated with membership in credit groups. These effects are confirmed by hierarchical random effects models, which control for demographic factors. This suggests that multiple scales of social capital—both within and outside the household—are correlated with household food security. This social capital may both be bridging (across groups) and bonding (within groups) with different implications for how social capital structure affects food security. Efforts to improve food security could recognize the potential for both household and community level social networks and collaboration, which further research can capture by analyzing multiple scales of social capital data.

Abstract. Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27+UK). We integrate recent emission inventory data, ecosystem process-based model results, and inverse modelling estimates over the period 1990–2018. BU and TD products are compared with European National GHG Inventories (NGHGI) reported to the UN climate convention secretariat UNFCCC in 2019. For uncertainties, we used for NGHGI the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines recommendations. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model specific uncertainties when reported. In comparing NGHGI with other approaches, a key source of bias is the activities included, e.g. anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. TD total inversions estimates give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions and geological sources together account for the gap between NGHGI and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1 respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1 respectively, compared to 0.9 Tg N2O yr−1 from the BU data. The TU and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at EU+UK scale and at national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288969 (Petrescu et al., 2020).

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

One of the major environmental problems is the fast accumulation of brick kiln emissions near the peripheries of almost all urban centers. An enormous quantity of substandard coal, wood, rubber tires, or saw wood is used as fuel in these brick kilns, creating environmental pollution through carbon and heavy metals present in the emissions. The present study was designed to determine the distribution pattern of heavy metals in a soil-water-plant system under the influence of brick kiln emissions. Ash, soil (0-15 cm), water, and plant samples were collected from the adjacent areas of the brick kilns. Geo-accumulation index and contamination factors were used to assess the toxicity of metals to soil and plants. It was found that the concentrations of Cd in water, soil, coal, and plant samples were found in the range of 0.004-0.024 mg L-1, 0.02-0.66, 0.002-0.042, and 0.06 to 0.07 mg kg-1, respectively, while Pb concentration was found within the range of 0-0.08 mg L-1, 0.30-8.60, 0.011-0, and 0.01-0.08 mg kg-1 in water, soil, and plant samples, respectively. The concentration of Cd in brick kilns ash was in the range of 0-0.04 mg kg-1 while Pb was found within the range of 0.01-0.05 mg kg-1 for ash. Geo-accumulation index and contamination factor values for Cd were found higher suggesting proper pollution prevention measures to be implemented. Based on the results, it was concluded that Cd concentrations in soil, plants, and water samples were higher in the subject area due to brick kiln emissions and higher Cd concentrations in the fuel material.

Sufficient production, consistent food supply, and environmental protection in urban +settings are major global concerns for future sustainable cities. Currently, sustainable food supply is under intense pressure due to exponential population growth, expanding urban dwellings, climate change, and limited natural resources. The recent novel coronavirus 2019 (COVID-19) pandemic crisis has impacted sustainable fresh food supply, and has disrupted the food supply chain and prices significantly. Under these circumstances, urban horticulture and crop cultivation have emerged as potential ways to expand to new locations through urban green infrastructure. Therefore, the objective of this study is to review the salient features of contemporary urban horticulture, in addition to illustrating traditional and innovative developments occurring in urban environments. Current urban cropping systems, such as home gardening, community gardens, edible landscape, and indoor planting systems, can be enhanced with new techniques, such as vertical gardening, hydroponics, aeroponics, aquaponics, and rooftop gardening. These modern techniques are ecofriendly, energy- saving, and promise food security through steady supplies of fresh fruits and vegetables to urban neighborhoods. There is a need, in this modern era, to integrate information technology tools in urban horticulture, which could help in maintaining consistent food supply during (and after) a pandemic, as well as make agriculture more sustainable.

According to the United Nations Framework Convention on Climate Change (UNFCCC) and Kyoto Protocol under it, industrial countries have to estimate their greenhouse gas emissions annually, and assess the uncertainties in these estimates. In Finland, agricultural methane (CH4) and nitrous oxide (N2O) emissions represent 7% of anthropogenic greenhouse gas emissions, and globally the share is much higher. Agriculture is one of the most uncertain emission categories (representing over 20% of greenhouse gas inventory uncertainty in Finland), due to both high natural variability of the emission sources and poor knowledge of the emission-generating processes. In this paper, we present an uncertainty estimate of agricultural CH4 and N2O emissions from Finland in 2002. Uncertainties were estimated based on measurement data, literature and expert judgement, and total uncertainty in agriculture was calculated using Monte Carlo simulation. According to the calculations, agricultural CH4 and N2O emissions from Finland were 3.7 to 7.8 Tg carbon dioxide (CO2) equivalents, 5.4 Tg being the mean value.Estimates of CH4 emissions are more reliable than those of N2O. N2O from agricultural soils was the most uncertain emission category, and the uncertainty was not reduced by using available national measurement data of N2O fluxes. Sensitivity study revealed that the uncertainty in total agricultural inventory could be 7% points lower, if more accurate emission estimation methods were used, including 1) improved data collection in area estimates of organic soils, 2) climate-specific methods for N2O from agricultural soils as already presented in literature, and 3) more detailed CH4 estimation methods for enteric fermentation which can be achieved by investigating national circumstances and digestible systems of animals in more detail.

Pakistan is an energy deficient country with depleting energy reserves and increasing energy demand. Due to excessive population growth, the domestic and commercial energy sectors are experiencing rising demand. To meet the requisite demand, renewables are favored rather than conventional counterparts. In this study, we model hybrid power systems using solar, wind and biomass resources for electrifying remote areas. The four locations are chosen for the study around a developing country, Pakistan, where each site is designed according to an isolated microgrid with maximum indigenous resources potential as per the requisite demands. A survey is conducted for the load demand and biomass availability. Optimization is conducted across objectives of minimum levelized cost of the generated energy, least the net present cost and lesser payback period. The optimal results were achieved in-terms of required objectives across southern sites as compared to northern counterparts. The cost of generated energy is comparable to grid electricity and ensures 24 h power supply without cut off and load shedding to the un-electrified rural area. The hybrid power system has a low carbon footprint across emissions due to the use of renewable resources. All the estimated load of rural communities is met with the available resources and mid-career impact has also been conducted across 10 years of the project life to fulfill the increasing load demand of the communities after installation. The results are validated via comparative analysis and show the effectiveness of the proposed study.

Analyzing the efficiency of the water, energy, and food (WEF) nexus is critical for effective governance strategies. Therefore, three-stage data envelopment analysis (DEA) was used to measure the efficiency level of WEF in the 36 districts of Punjab, Pakistan, for the period from 2015 to 2021. Furthermore, the stochastic frontier was used to analyze the effect of external environmental factors on these efficiency scores of the WEF nexus. The results of the DEA showed that the number of frontier efficiency districts decreased, and most districts experienced rank change over time. Overall, the performance of 50% of the districts declined over time. The relative decline in efficiency was found to be higher in districts Bahwalnaghar and Rahim Yar Khan. The performance of districts Multan and Sheikhupura increased over time, while districts Vehari and Sargodha were the most complete and efficient in actual performance. According to the SFA’s findings, the WEF nexus efficiency of South Punjab districts was negatively impacted by external environmental factors (urbanization rate, manufactured industry output, population), leading to severe stress across WEF sectors. Districts in central and southern Punjab, however, were more likely to have lower rankings because of the positive impact of external environmental factors on the efficiency of the WEF nexus. The substantial rise of external environmental variables focused on scale expansion rather than quality improvement, which created a wide gap in WEF inputs and, hence, reduced the efficiency of the WEF nexus in the districts. The findings of this study provide valuable insights for developing governance strategies based on external environmental factors and WEF resource endowment, and they complement the efficiency calculation of WEF nexus research. Future research should focus on the Baluchistan region, the most deprived area in terms of water, energy, and food.