In 2021, the US and EU launched the Global Methane Pledge aiming to reduce emissions by 30% below 2020 levels by 2030 (about 2,985 MtCO 2 e). Since its launch, 160 countries have joined the initiative. With less than five years remaining to achieve this target—and despite evolving political landscapes and shifting national priorities—identifying cost-effective mitigation strategies remains critically important. Given the considerable variation in methane abatement potential and costs across countries and sectors, a disaggregated global assessment is essential. This study focuses on direct methane abatement activities as a key component of the mitigation portfolio. Results indicate that 73% of the Pledge (about 2,400 MtCO 2 e) can be achieved through technological interventions costing less than 100$/tCO 2 e by 2030, with the energy sector offering the largest potential. These findings underscore the feasibility of targeted technological action and highlight the regional differences in abatement potential that shape progress toward global methane reduction goals within the remaining timeframe.

The Energy Modeling Forum 37 study is organized around carbon dioxide (CO2) mitigation scenarios reaching net-zero CO2 emissions by 2050 in the United States. This paper summarizes the potential contribution of bioenergy use in the electric power, transportation, industrial, and buildings sectors toward meeting that target based on model results. Thirteen modeling teams reported bioenergy consumption in the Reference and Net Zero scenarios. Consumption of bioenergy increased over time in the Reference scenario, from an average across models of 3.2 exajoules (EJ) in 2020 to 3.8 EJ in 2050. Average bioenergy consumption in 2050 increased further to 7.3 EJ in the Net Zero scenario. All scenarios that reach net-zero emissions required some form of carbon dioxide removal to offset emissions that are difficult to reduce. Carbon dioxide removal using bioenergy with CO2 capture and storage (BECCS) varies widely across models, up to 1000 Mt CO2 in 2050. Some models rely instead on direct air carbon capture and storage (DACCS), up to 2200 Mt CO2, and others use a combination of BECCS and DACCS. Model results show a strong inverse relationship between the amounts of BECCS and DACCS deployed. All modeling teams assumed a carbon sink from land use, land use change, and forestry, further offsetting a portion of emissions from fossil fuels and industry that are expensive to eliminate. Bioenergy consumption in 2050 decreased by an average of 1.5 EJ across eight models in a Net Zero+ scenario relative to the Net Zero scenario, due in part to a lower equilibrium carbon price resulting from optimistic cost assumptions for all energy technologies.

Computable general equilibrium (CGE) models provide valuable insights into economy-wide impacts of anticipated future structural changes in the transportation sector, yet few CGE models offer detailed transportation representations. We use an enhanced Applied Dynamic Analysis of the Global Economy (ADAGE) CGE model to incorporate disaggregated transportation modes and technologies in on-road passenger and freight transportation. We assess the impacts of these inclusions on U.S. transportation patterns, energy consumption and greenhouse gas emissions. Simulating illustrative global oil price cases with and without transportation detail, we find subsector mode disaggregation and technology additions in a CGE model significantly alter the impacts of oil prices on global trade and freight patterns, energy consumption, and greenhouse gas (GHG) emissions. We find that: (1) alternative technologies are essential for capturing transportation sector impacts, (2) electrification may reduce emissions with electricity decarbonization, and (3) higher oil prices may hasten electrification.

Better targeting of fertilizer application can result in yields that meet or exceed those achieved without improved targeting while reducing total fertilizer use. However, in many growing regions, it may be difficult for farmers to access the necessary data to inform improved fertilizer use. The GreenSeeker Handheld is a low-cost technology that can be used to improve the efficiency of fertilizer applications by providing farmers with recommendations for the amount of nitrogen (N) to apply mid-season to meet the needs of their crops. The technology has been utilized on nearly 2,000 farmer fields representing more than 60,000 hectares in three regions of Mexico. In this study, the net effects of this technology on economic and environmental outcomes for these farmers were assessed. Specifically, farmer field-level data and locally derived greenhouse gas emission factors were analyzed and use of the GreenSeeker Handheld was estimated to have led to a total of $2.6 M USD in additional profits and more than 14,300 tons CO 2 e of avoided emissions. However, not all farmers utilizing this technology followed the resulting recommendations. Participating farmers that did not follow the recommendations experienced lower profits, indicating that benefits would have been larger had participants applied fertilizer at the levels recommended for their fields. In addition, the total benefits of higher farm profits and reduced emissions could be scaled up significantly if this technology were applied more broadly. However, development and implementation of the technology had been supported by subsidies and further efforts would be needed to make it sustainable.

Air pollution poses major disease burdens globally and accounts for approximately 10% of deaths annually through its contribution to a variety of respiratory, cardiovascular, and other diseases. The burden of disease is particularly acute in Saudi Arabia, where a mix of anthropogenic and natural sources of air pollution threatens public health. Addressing these burdens requires careful study of the costs and effectiveness of available technologies and policies for reducing emissions (mitigation) and avoiding exposure (adaptation). To help evaluate these options, we conduct a semi-systematic literature review of over 3,000 articles published since 2010 that were identified by searches of literature focused on pollution mitigation and pollution adaptation. We identify a wide variety of effective mitigation and adaptation technologies and find that cost-effectiveness information for policy design is highly variable in the case of mitigation, both within and across pollution source categories; or scarce, in the case of adaptation. While pollution control costs are well studied, policy costs differ; these may vary more by location because of factors such as technology operating conditions and behavioral responses to adaptation initiatives, limiting the generalizability of cost-effectiveness information. Moreover, potential cost advantages of multipollutant control policies are likely to depend on the existing mix of pollution sources and controls. While the policy literature generally favors more flexible compliance mechanisms that increase the cost of polluting to reflect its costs to society, important policy design factors include policy co-benefits, distributional concerns, and inter-regional harmonization. In addition to these key themes, we find that further study is needed both to improve the availability of cost information for adaptation interventions and to localize technology and policy cost estimates to the Saudi context.

Solar home systems (SHS), a cost-effective solution for rural communities far from the grid in developing countries, are small solar panels and associated equipment that provides power to a single household. A crucial resource for targeting further investment of public and private resources, as well as tracking the progress of universal electrification goals, is shared access to high-quality data on individual SHS installations including information such as location and power capacity. Though recent studies utilizing satellite imagery and machine learning to detect solar panels have emerged, they struggle to accurately locate many SHS due to limited image resolution (some small solar panels only occupy several pixels in satellite imagery). In this work, we explore the viability and cost-performance tradeoff of using automatic SHS detection on unmanned aerial vehicle (UAV) imagery as an alternative to satellite imagery. More specifically, we explore three questions: (i) what is the detection performance of SHS using drone imagery; (ii) how expensive is the drone data collection, compared to satellite imagery; and (iii) how well does drone-based SHS detection perform in real-world scenarios? To examine these questions, we collect and publicly-release a dataset of high-resolution drone imagery encompassing SHS imaged under a variety of real-world conditions and use this dataset and a dataset of imagery from Rwanda to evaluate the capabilities of deep learning models to recognize SHS, including those that are too small to be reliably recognized in satellite imagery. The results suggest that UAV imagery may be a viable alternative to identify very small SHS from perspectives of both detection accuracy and financial costs of data collection. UAV-based data collection may be a practical option for supporting electricity access planning strategies for achieving sustainable development goals and for monitoring the progress towards those goals.

BACKGROUND:Climate change presents an increasing challenge for food-nutrition security. Nutrition metrics calculated from quantitative food system projections can help focus policy actions. OBJECTIVES:To estimate future chronic and hidden hunger disability-adjusted life years (DALYs)-due to protein-energy undernutrition and micronutrient deficiencies, respectively-using food systems projections to evaluate the potential impact of climate change and agricultural sector investment for adaptation. METHODS:We use a novel combination of a chronic and hidden hunger DALY estimation procedure and food system projections from quantitative foresight modeling to assess DALYs under alternative agricultural sector scenarios to midcentury. RESULTS:Total chronic and hidden hunger DALYs are projected to increase globally out to 2050-by over 30 million compared with 2010-even without climate change. Climate change increases total DALY change between 2010 and 2050 by nearly 10% compared with no climate change. Agricultural sector investments show promise for offsetting these impacts. With investments, DALY incidence due to chronic and hidden hunger is projected to decrease globally in 2050 by 0.24 and 0.56 per 1000 capita, respectively. Total global DALYs will still rise because projected population growth will outpace the rate reduction, especially in Africa south of the Sahara. However, projections also show important regional reductions in total DALYs due to chronic (13.9 million in South Asia, 4.3 million in East Asia and the Pacific) and hidden hunger (7.5 million in East Asia and the Pacific) with investments. CONCLUSIONS:Food system projections to 2050 show a decreasing DALY incidence from both chronic and hidden hunger. Population growth is projected to outpace these improvements and lead to increasing total chronic and hidden hunger DALYs globally, concentrated in Africa south of the Sahara. Climate change increases per-capita chronic and hidden hunger DALY incidence compared with no climate change. Agricultural sector investments show the potential to offset the climate impact on DALYs.

Land‐based climate mitigation measures have gained significant attention and importance in public and private sector climate policies. Building on previous studies, we refine and update the mitigation potentials for 20 land‐based measures in >200 countries and five regions, comparing “bottom‐up” sectoral estimates with integrated assessment models (IAMs). We also assess implementation feasibility at the country level. Cost‐effective (available up to $100/tCO 2 eq) land‐based mitigation is 8–13.8 GtCO 2 eq yr −1 between 2020 and 2050, with the bottom end of this range representing the IAM median and the upper end representing the sectoral estimate. The cost‐effective sectoral estimate is about 40% of available technical potential and is in line with achieving a 1.5°C pathway in 2050. Compared to technical potentials, cost‐effective estimates represent a more realistic and actionable target for policy. The cost‐effective potential is approximately 50% from forests and other ecosystems, 35% from agriculture, and 15% from demand‐side measures. The potential varies sixfold across the five regions assessed (0.75–4.8 GtCO2eq yr −1 ) and the top 15 countries account for about 60% of the global potential. Protection of forests and other ecosystems and demand‐side measures present particularly high mitigation efficiency, high provision of co‐benefits, and relatively lower costs. The feasibility assessment suggests that governance, economic investment, and socio‐cultural conditions influence the likelihood that land‐based mitigation potentials are realized. A substantial portion of potential (80%) is in developing countries and LDCs, where feasibility barriers are of greatest concern. Assisting countries to overcome barriers may result in significant quantities of near‐term, low‐cost mitigation while locally achieving important climate adaptation and development benefits. Opportunities among countries vary widely depending on types of land‐based measures available, their potential co‐benefits and risks, and their feasibility. Enhanced investments and country‐specific plans that accommodate this complexity are urgently needed to realize the large global potential from improved land stewardship.

Accurate projections of seasonal agricultural output are essential for improving food security. However, the collection of agricultural information through seasonal agricultural surveys is often not timely enough to inform public and private stakeholders about crop status during the growing season. Acquiring timely and accurate crop estimates can be particularly challenging in countries with predominately smallholder farms because of the large number of small plots, intense intercropping, and high diversity of crop types. In this study, we used RGB images collected from unmanned aerial vehicles (UAVs) flown in Rwanda to develop a deep learning algorithm for identifying crop types, specifically bananas, maize, and legumes, which are key strategic food crops in Rwandan agriculture. The model leverages advances in deep convolutional neural networks and transfer learning, employing the VGG16 architecture and the publicly accessible ImageNet dataset for pretraining. The developed model performs with an overall test set F1 of 0.86, with individual classes ranging from 0.49 (legumes) to 0.96 (bananas). Our findings suggest that although certain staple crops such as bananas and maize can be classified at this scale with high accuracy, crops involved in intercropping (legumes) can be difficult to identify consistently. We discuss the potential use cases for the developed model and recommend directions for future research in this area.

Addressing the grand challenge of achieving global food security for a growing population within land and other resource constraints requires significant advances in production efficiency and sustainability. Timely and accurate agricultural information is needed to inform resource allocation and sustainable farming practices leading to improved food security for the developing world. Systems relying on satellite-based data have the near-term potential to provide stakeholders with information on crop production, status, and predicted yield that is timely, covers large areas, captures spatial and temporal detail, and can be obtained at low cost, potentially contributing to better decisions. Machine-learning models have been used to create cropped land area maps using freely available satellite data, but these models are heavily dependent on the quality of the training data. For example, in applications focused on identifying areas of individual crop types, ground-truth locations must be georeferenced accurately and labeled correctly. Achieving this accuracy is difficult in smallholder agricultural systems, especially when fields are intercropped. In this pilot study, we used imagery acquired from unmanned aerial vehicles to create a high-fidelity ground-truth dataset that was used to train and test a random forest model for classifying cropped land using freely available Sentinel-1 and -2 data. These data are used to estimate area cultivated, identify major crops, and assess crop growth over time during the growing season. This model achieved an overall accuracy of 83%, and a 91% accuracy for maize specifically. The model results were also compared with the seasonal agricultural survey which highlighted biases in the dataset including a lack of examples of cells with mixed land cover.

Limiting global warming in line with the goals in the Paris Agreement will require substantial technological and behavioural transformations. This challenge drives many of the current modelling trends. This article undertakes a review of 17 state-of-the-art recursive-dynamic computable general equilibrium (CGE) models and assesses the key methodologies and applied modules they use for representing sectoral energy and emission characteristics and dynamics. The purpose is to provide technical insight into recent advances in the modelling of current and future energy and abatement technologies and how they can be used to make baseline projections and scenarios 20-80 years ahead. Numerical illustrations are provided. In order to represent likely energy system transitions in the decades to come, modern CGE tools have learned from bottom-up studies. Three different approaches to baseline quantification can be distinguished: (a) exploiting bottom-up model characteristics to endogenize responses of technological investment and utilization, (b) relying on external information sources to feed the exogenous parameters and variables of the model, and (c) linking the model with more technology-rich, partial models to obtain bottom-up- and pathway-consistent parameters.

When modeling medium and long-term challenges we need a reference path of economic development (the so-called baseline). Because sectoral models often offer a more fundamental understanding of future developments for specific sectors, many CGE modeling teams have adopted approaches for linking their models to sectoral models to generate baselines. Linked models include agricultural sector, energy sector, biophysical and macroeconomic models. We systematically compare and discuss approaches of linking CGE models to sectoral models for the baseline calibration procedure and discuss challenges and best practices. We identify different types of linking approaches which we divide into a) one-way, and b) two-way linking. These two types of linking approaches are then analyzed with respect to the degree of consistency of the linkage, information exchanged, as well as compromises in aggregations and definitions. Based on our assessment, we discuss challenges and conclude with suggestions for best practices and research recommendations.

This study quantifies the potential responses of 11 staple crop yields to projected changes in temperature and precipitation in Rwanda, using a cross sectional model based on yield data collected across more than 14,000 villages. We incorporated a relatively high spatial resolution dataset on crop productivity, considered a broad range of crops relevant to national agricultural production priorities, used environmental data developed specifically for Rwanda, and reported uncertainty both from our estimation model and due to uncertainty in future climate projections. We estimate that future climate change will have the largest impacts on potential productivity of maize, bush bean, and Irish potato. All three crops are likely to experience a reduction in potential yields of at least 10% under Representative Concentration Pathway (RCP) 4.5 and at least 15% under RCP 8.5 by 2050. Notably, these are important crops nationally, and three of the crops targeted by Rwanda's Crop Intensification Program. We find that the most severe reductions in potential crop yields will occur in the drier eastern savannah and plateau regions, but that the impacts of climate change could be neutral or even positive in the highlands through mid-century. The refined spatial scale of our analysis allows us to identify potentially vulnerable regions where adaptation investments may need to be prioritized to support food security and climate resilience in Rwanda's agricultural sector.

Timely and accurate agricultural information is needed to inform resource allocation and sustainable practices to improve food security in the developing world. Obtaining this information through traditional surveys is time consuming and labor intensive, making it difficult to collect data at the frequency and resolution needed to accurately estimate the planted areas of key crops and their distribution during the growing season. Remote sensing technologies can be leveraged to provide consistent, cost-effective, and spatially disaggregated data at high temporal frequency. In this study, we used imagery acquired from unmanned aerial vehicles to create a high-fidelity ground-truth dataset that included examples of large mono-cropped fields, small intercropped fields, and natural vegetation. The imagery was acquired in three rounds of flights at six sites in different agro-ecological zones to capture growing conditions. This dataset was used to train and test a random forest model that was implemented in Google Earth Engine for classifying cropped land using freely available Sentinel-1 and -2 data. This model achieved an overall accuracy of 83%, and a 91% accuracy for maize specifically. The model results were compared with Rwanda’s Seasonal Agricultural Survey, which highlighted biases in the dataset including a lack of examples of mixed land cover.

Addressing the grand challenge of achieving global food security for a growing population within land and other resource constraints requires significant advances in production efficiency and sustainability. Systems relying on satellite-based data have the near-term potential to provide stakeholders with information on crop production, status, and predicted yield that is timely, covers large areas, captures spatial and temporal detail, and can be obtained at low cost, potentially contributing to better decisions. We develop methods and tools to acquire and process geospatial information from multiple data streams and apply to analyses of Rwandan agriculture. We combine satellite imagery with images collected using unmanned aerial vehicles and field survey data and apply machine learning techniques to provide rapid, consistent image processing and analysis. These data are used to estimate area cultivated, identify major crops, and assess crop growth over time during the growing season.

Although Rwanda has accomplished significant improvements in food production in recent years, one fifth of its population remains food insecure. Agricultural information is currently collected through seasonal agricultural surveys, but more frequent and timely data collection is needed to adequately inform public and private decision-makers about the status of crops during the growing season. Sentinel-1 and -2 data are freely available with new images provided every 4-5 days. While analysis of these multispectral images has been used for agricultural applications, there are few applications to smallholder agriculture. Major challenges for satellite image analysis in the context of Rwanda include heavily clouded scenes and small plot sizes that are often intercropped. Sentinel-2 scenes corresponding to mid-season were analyzed, and spectral signatures of maize could be distinguished from those of other crops. Seasonal mean filtering was applied to Sentinel-1 scenes, and there was significant overlap in the spectral signatures across different types of vegetation. Random Forest models for classification of Sentinel scenes were developed using a training dataset that was constructed from high-resolution multispectral images acquired by unmanned aerial vehicles (UAVs) in several different locations in Rwanda and labeled as to the crop type by trained observers. The models were applied to satellite images of the whole country of Rwanda and validated using a test dataset from the UAV images. The Sentinel-2 model had the user's accuracy for maize classification of 75%, while the Sentinel-1 model overestimated the maize area resulting in a user's accuracy of <50%.

Background Increasing atmospheric concentrations of carbon dioxide (CO2) affect global nutrition via effects on agricultural productivity and nutrient content of food crops. We combined these effects with economic projections to estimate net changes in nutrient availability between 2010 and 2050. Methods In this modelling study, we used the International Model for Policy Analysis of Agricultural Commodities and Trade to project per capita availability of protein, iron, and zinc in 2050. We used estimated changes in productivity of individual agricultural commodities to model effects on production, trade, prices, and consumption under moderate and high greenhouse gas emission scenarios. Two independent sources of data, which used different methodologies to determine the effect of increased atmospheric CO2 on different key crops, were combined with the modelled food supply results to estimate future nutrient availability. Findings Although technological change, market responses, and the effects of CO2 fertilisation on yield are projected to increase global availability of dietary protein, iron, and zinc, these increases are moderated by negative effects of climate change affecting productivity and carbon penalties on nutrient content. The carbon nutrient penalty results in decreases in the global availability of dietary protein of 4.1%, iron of 2.8%, and zinc of 2.5% as calculated using one dataset, and decreases in global availability of dietary protein of 2.9%, iron of 3.9%, and zinc of 3.4% using the other dataset. The combined effects of projected increases in atmospheric CO2 (ie, carbon nutrient penalty, CO2 fertilisation, and climate effects on productivity) will decrease growth in the global availability of nutrients by 19.5% for protein, 14.4% for iron, and 14.6% for zinc relative to expected technology and market gains by 2050. The many countries that currently have high levels of nutrient deficiency would continue to be disproportionately affected. Interpretation This approach is an improvement in estimating future global food security by simultaneously projecting climate change effects on crop productivity and changes in nutrient content under increased concentrations of CO2, which accounts for a much larger effect on nutrient availability than CO2 fertilisation. Regardless of the scenario used to project future consumption patterns, the net effect of increasing concentrations of atmospheric CO2 will slow progress in decreasing global nutrient deficiencies. Copyright (C) 2019 The Author(s). Published by Elsevier Ltd.

In recent decades, the carbon sink provided by the U.S. forest sector has offset a sizable portion of domestic greenhouse gas (GHG) emissions. In the future, the magnitude of this sink has important implications not only for projected U.S. net GHG emissions under a reference case but also for the cost of achieving a given mitigation target. The larger the contribution of the forest sector towards reducing net GHG emissions, the less mitigation is needed from other sectors. Conversely, if the forest sector begins to contribute a smaller sink, or even becomes a net source, mitigation requirements from other sectors may need to become more stringent and costlier to achieve economy wide emissions targets. There is acknowledged uncertainty in estimates of the carbon sink provided by the U.S. forest sector, attributable to large ranges in the projections of, among other things, future economic conditions, population growth, policy implementation, and technological advancement. We examined these drivers in the context of an economic model of the agricultural and forestry sectors, to demonstrate the importance of cross-sector interactions on projections of emissions and carbon sequestration. Using this model, we compared detailed scenarios that differ in their assumptions of demand for agriculture and forestry products, trade, rates of (sub)urbanization, and limits on timber harvest on protected lands. We found that a scenario assuming higher demand and more trade for forest products resulted in increased forest growth and larger net GHG sequestration, while a scenario featuring higher agricultural demand, ceteris paribus led to forest land conversion and increased anthropogenic emissions. Importantly, when high demand scenarios are implemented conjunctively, agricultural sector emissions under a high income-growth world with increased livestock-product demand are fully displaced by substantial GHG sequestration from the forest sector with increased forest product demand. This finding highlights the potential limitations of single-sector modeling approaches that ignore important interaction effects between sectors.