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The role of hydrogen in the transition to carbon-neutral energy systems will be influenced by key factors such as carbon neutrality pathways, hydrogen production technology costs, and hydrogen transportation costs. Existing studies have not comprehensively analyzed and compared the impact of these key factors on the development of hydrogen supply and demand under China's carbon neutrality pathways. This study uses the Global Change Assessment Model (GCAM) with an upgraded hydrogen module to evaluate the development potential of China's hydrogen industry, considering various carbon neutrality pathways as well as hydrogen production and transportation costs. The findings indicate that, by 2050, hydrogen could account for 8%–14% of final energy, averting 1.0–1.7 Bt of carbon emissions annually at an average mitigation cost of 85–183 USD t−1CO2. The total hydrogen production is projected to reach 75–135 Mt, with 34%–56% from renewable energy electrolysis and about 15%–29% from fossil fuel-based CCS. On a sectoral level, by 2050, the hydrogen demand in the industrial and transportation sectors is expected to reach 37–63 Mt and 30–42 Mt, with a potential reduction of about 0.6–0.9 BtCO2 and 0.5–0.6 BtCO2. The share of hydrogen in the final energy of the steel and chemical sectors is estimated to be 9%–19% and 17%–25%, collectively accounting for 36%–42% of total hydrogen demand and 46%–50% of total emission reduction potential. Realizing hydrogen's emission reduction potential relies on the rapid development of hydrogen production, transportation, and utilization technologies. Firstly, the development of on-site electrolysis for hydrogen production and early deployment of industrial hydrogen applications should be prioritized to stimulate overall growth of hydrogen industry and cost reduction. Secondly, vigorous development of renewable energy electrolysis and hydrogen end-use technologies like fuel cells should be pursued, along with the demonstration and promotion of hydrogen transportation technologies. Lastly, further advancement of carbon market mechanisms is essential to support the widespread adoption of hydrogen technologies.

We quantify the potential impacts of carbon capture, utilization, and storage (CCUS) on local air pollution in the US Gulf region. We characterize pre-CCUS local air pollution emissions from fossil-fuel electricity generation and large industrial carbon emitters and compare these with projected post-CCUS local air pollution emissions, leveraging carbon capture quotient estimates from the engineering literature. We then analyze net emissions impacts on secondary particulate matter formation and damages via the AP3 integrated air pollution assessment model. An important aspect of the environmental impact of CCUS is increased ammonia emissions from CCUS technology, which we show can lead to net air pollution damages from CCUS deployment at natural gas power plants and most industrial facilities. Taken as a whole, our work suggests that CCUS may pose significant trade-offs between global climate benefits and costs to local communities. [ABSTRACT FROM AUTHOR]

Developing transformative pathways for industry's compliance with international climate targets requires model-based insights into how supply- and demand-side measures affect industry, material cycles, global supply chains, socioeconomic activities, and service provisioning that support societal well-being. We review the recent literature modeling the industrial system in low energy and material demand futures, which mitigates environmental impacts without relying on risky future negative emissions and technological fixes. We identify 77 innovative studies drawing on nine distinct industry modeling traditions. We critically assess system definitions and scopes, biophysical and thermodynamic consistency, granularity and heterogeneity, and operationalization of demand and service provisioning. We find that combined supply- and demand-side measures could reduce current economy-wide material use by 56%, energy use by 40% to 60%, and greenhouse gas emissions by 70% to net zero. We call for strengthened interdisciplinary collaborations between industry modeling traditions and demand-side research to produce more insightful scenarios, and we discuss challenges and recommendations for this emerging field. [ABSTRACT FROM AUTHOR]

Despite decades of policy that strives to reduce nutrient and sediment export from agricultural fields, surface water quality in intensively managed agricultural landscapes remains highly degraded. Recent analyses show that current conservation efforts are not sufficient to reverse widespread water degradation in Midwestern agricultural systems. Intensifying row crop agriculture and increasing climate pressure require a more integrated approach to water quality management that addresses diverse sources of nutrients and sediment and off-field mitigation actions. We used multiobjective optimization analysis and integrated three biophysical models to evaluate the cost-effectiveness of alternative portfolios of watershed management practices at achieving nitrate and suspended sediment reduction goals in an agricultural basin of the Upper Midwestern United States. Integrating watershed-scale models enabled the inclusion of near-channel management alongside more typical field management and thus directly the comparison of cost-effectiveness across portfolios. The optimization analysis revealed that fluvial wetlands (i.e., wide, slow-flowing, vegetated water bodies within the riverine corridor) are the single-most cost-effective management action to reduce both nitrate and sediment loads and will be essential for meeting moderate to aggressive water quality targets. Although highly cost-effective, wetland construction was costly compared to other practices, and it was not selected in portfolios at low investment levels. Wetland performance was sensitive to placement, emphasizing the importance of watershed scale planning to realize potential benefits of wetland restorations. We conclude that extensive interagency cooperation and coordination at a watershed scale is required to achieve substantial, economically viable improvements in water quality under intensive row crop agricultural production.

Our purpose is to advance a reasoned perspective on the scientific validity of computer simulation, using an example—integrated assessment modeling of climate change and its projected impacts—that is itself of great and urgent interest to policy in the real world. The spirited and continuing debate on the scientific status of integrated assessment models (IAMs) of global climate change has been conducted mostly among climate change modelers and users seeking guidance for climate policy. However, it raises a number and variety of issues that have been addressed, with various degrees of success, in other literature. The literature on methodology of simulation was mostly skeptical at the outset but has become more nuanced, casting light on some key issues relating to the validity and evidentiary standing of climate change IAMs (CC-IAMs). We argue that the goal of validation is credence, i.e., confidence or justified belief in model projections, and that validation is a matter of degree: (perfect) validity is best viewed as aspirational and, other things equal, it makes sense to seek more rather than less validation. We offer several conclusions. The literature on computer simulation has become less skeptical and more inclined to recognize that simulations are capable of providing evidence, albeit a different kind of evidence than, say, observation and experiments. CC-IAMs model an enormously complex system of systems and must respond to several challenges that include building more transparent models and addressing deep uncertainty credibly. Drawing on the contributions of philosophers of science and introspective practitioners, we offer guidance for enhancing the credibility of CC-IAMs and computer simulation more generally.

Deep human‐Earth system uncertainties and strong multi‐sector dynamics make it difficult to anticipate which conditions are most likely to lead to higher or lower adoption of renewable energy, and models project a broad range of future solar and wind energy shares across future scenarios. To elucidate these dynamics, we explore a large data set of scenarios simulated from the Global Change Analysis Model (GCAM), and use scenario discovery to identify the most significant factors affecting solar and wind adoption by mid‐century. We generated a data set of over 4,000 scenarios from GCAM by varying 12 different socioeconomic factors at high and low levels, including assumptions about future energy demand, resource costs, and fossil fuel emissions paths, as well as specific technology assumptions including wind and solar backup requirements and storage costs. Using scenario discovery, we assess the most important factors globally and regionally in creating high fractions of solar and wind energy and explore interconnected effects on other systems including water and non‐CO2 emissions. Globally and regionally, we found that solar and wind‐related technology costs were the primary drivers of high wind and solar energy adoption, though a few regions depend heavily on other parameters like carbon capture and storage costs, population and gross domestic product trajectories, and fossil fuel costs. We also identify four key paths to high solar and wind energy by mid‐century and discuss their tradeoffs in terms of other outcomes. Plain Language Summary: There are many systems involved in energy transitions, which makes it difficult to anticipate which factors are most likely to result in higher renewable energy adoption in the future, and the currently available projections of future renewable shares are highly uncertain. We focus here on wind and solar energy in particular, and use a model that represents a variety of the different systems involved (including energy, agriculture, land use, and water) to create a set of nearly 4,000 scenarios that span a wide range of possible futures. Each scenario is driven by a combination of different parameter inputs chosen based on factors that we expect to impact wind and solar energy shares. By analyzing this set of scenarios, we can find the most important drivers, and combinations of drivers, globally and in each of the 32 different regions represented in our model. Additionally, we look at the scenarios that produced the highest fractions of wind and solar energy and identify four different combinations of parameters that can lead to these high renewable fractions. For each of the four paths, we explore the implications in terms of outcomes like water consumption, air pollution, and food prices, and discuss the resulting tradeoffs. Key Points: Solar and wind energy transitions are complex multi‐sectoral problemsWhile globally solar and wind technology costs drive renewable shares, regionally different drivers become more importantDifferent combinations of drivers can result in similar high solar and wind energy but have very different implications across other sectors [ABSTRACT FROM AUTHOR]

Education for sustainable development has among its pillars, capacity building, which equips future generations with the set of skills needed to face the challenge of the transformation of society for sustainable development. This paper presents a training course for a novel model of long-term energy planning (the ModUlar energy system Simulation Environment, MUSE), as an example of capacity building activities for sustainable development. The activities were part of the Joint Summer School on Modelling Tools for Sustainable development, held in Trieste (Italy) in 2022. This summer school was one of the first successful implementations of education and training courses in a super-hybrid mode in the post-COVID era. Describing the training activities for MUSE open-source, this paper addresses one of the challenges that education for sustainable development is expected to increasingly face in the future: the training of future professionals in the use of novel toolkits and the implementation of truly trans-disciplinary approaches.This paper discusses the pre-school online training course for MUSE, the summer school contents, and some student modeling outcomes. While doing so, it shows the importance of leveraging the abstract contents of a course with practical exercises when learning a new tool. Reflecting upon the students' experience, this paper draws conclusions that can be used to improve future editions of the same course and be extended to the design of training courses for other tools. [ABSTRACT FROM AUTHOR]

Abstract Deep human‐Earth system uncertainties and strong multi‐sector dynamics make it difficult to anticipate which conditions are most likely to lead to higher or lower adoption of renewable energy, and models project a broad range of future solar and wind energy shares across future scenarios. To elucidate these dynamics, we explore a large data set of scenarios simulated from the Global Change Analysis Model (GCAM), and use scenario discovery to identify the most significant factors affecting solar and wind adoption by mid‐century. We generated a data set of over 4,000 scenarios from GCAM by varying 12 different socioeconomic factors at high and low levels, including assumptions about future energy demand, resource costs, and fossil fuel emissions paths, as well as specific technology assumptions including wind and solar backup requirements and storage costs. Using scenario discovery, we assess the most important factors globally and regionally in creating high fractions of solar and wind energy and explore interconnected effects on other systems including water and non‐CO2 emissions. Globally and regionally, we found that solar and wind‐related technology costs were the primary drivers of high wind and solar energy adoption, though a few regions depend heavily on other parameters like carbon capture and storage costs, population and gross domestic product trajectories, and fossil fuel costs. We also identify four key paths to high solar and wind energy by mid‐century and discuss their tradeoffs in terms of other outcomes.

Meyer (Environ Econ Policy Stud, 2022) questions a number of assumptions behind the social cost of carbon (SCC) calculations in Dayaratna et al. (Environ Econ Policy Stud 22:433–448, 2020), especially the CO2 fertilization benefit and the climate sensitivity estimate. He recommends against increasing the CO2 effect and suggests applying a recent climate sensitivity estimate in Lewis, Clim Dyn (2022), but did not calculate the resulting SCC distribution. Herein we critically assess his recommendations and compute the SCC distribution they imply. It has a median SCC value in 2050 of $3.39 and implies a 33.4 percent probability of the optimal carbon tax being negative. While a bit higher than the results in Dayaratna et al. (Environ Econ Policy Stud 22:433–448, 2020), they are not materially different for the purposes of setting optimal climate policy. [ABSTRACT FROM AUTHOR]

Dayaratna et al. (Environ Econ Policy Stud 22:433–448, 2020) proposes several improvements to economic- and climate-simulating IAMs and to their input variables. They show that an empirically based ECS estimate, agricultural damage estimates from recent literature, and discount rates as low as 2.5%, when used within the FUND model, yield a negative social cost of carbon through mid-twenty-first century. Five of their propositions, that "fat-tails" of the ECS distribution are improperly simulated by IAMS, FUND does not simulate enough benefits for agriculture, the ECS is near 1.5 °C, DICE and PAGE overestimate the SCC, and the marginal cost of public funds for carbon taxes has a value of at least a few dollars, are more closely analyzed. Estimates of ECS from a recent paper and a 5% discount rate applied to FUND result in an SCC estimated to lie within the range of about −$1 to $2.50 per metric ton CO2 for the year 2020. [ABSTRACT FROM AUTHOR]

The Latin America and the Caribbean (LAC) region plays key roles in both meeting global agricultural demands and maintaining carbon sinks due to its abundant land and water resources. In this study we use the Global Change Analysis Model to evaluate the opportunities and challenges posed by two global‐scale drivers: agricultural market integration (i.e., reduction of trade barriers) and land‐based climate mitigation policy. We evaluate their potential individual and combined impacts on agricultural production and trade revenues across LAC's economies through mid‐century, as well as the resulting impacts on agricultural consumers and integrated land‐water‐climate systems across LAC's diverse sub‐regions. Increased global market integration results in increased agricultural production and trade revenues for many LAC economies, driven by their evolving comparative advantages. Climate mitigation measures on CO2 and non‐CO2 greenhouse gases increase revenues due to increased agricultural prices from land competition and emissions abatement. The combined outcomes from both drivers are complex and sometimes non‐linear, highlighting the importance of understanding the interactions between multiple drivers. Our results show that increased agricultural production and trade opportunities, from either of the two drivers, pose significant trade‐offs that require careful multi‐sectoral planning, such as emissions reduction challenges, potential loss of livestock production when pursuing land‐based climate mitigation strategies, increased consumer expenditures, and changes in land‐use or water withdrawals, resulting in deforestation or water scarcity pressures. There is considerable heterogeneity in economic and environmental outcomes across LAC sub‐regions and agricultural commodities, illustrating the value of considering outcomes at finer scales. Key Points: Latin America plays a key role in meeting global agricultural demands in a world with more interconnected tradeClimate mitigation raises agricultural production prices and shifts livestock trade patterns based on emissions reduction potentialAgricultural production and trade opportunities have trade‐offs on increased consumer expenditures and impacts on land, water, and climate [ABSTRACT FROM AUTHOR]

Climate change is expected to impact crop yields globally, with some regions benefiting from favorable conditions and CO _2 fertilization, while others face adverse effects from altered precipitation and higher temperatures. Changes in crop yields can destabilize the global food system and pose challenges to food security. Moreover, crop production is crucial, as biofuels are becoming increasingly important contributors to climate change mitigation measures aimed at limiting global warming. This study uses the Integrated Model to Assess the Global Environment integrated assessment model framework to analyze different indicators related to food security and climate change mitigation under varying climate change impacts on crop yields. Twelve spatially explicit crop productivity projections were taken from the full archive of the Global Gridded Crop Model Intercomparison of 120 climate-crop model combinations, forced by CMIP6-based climate scenarios. The selection includes two average-performing climate-crop model combinations, two pessimistic combinations that perform one standard deviation below the mean, and two optimistic model combinations that perform one standard deviation above the mean. To single out the effect of climate change on productivity changes, we drew samples from two representative concentration pathways (RCP2.6 and RCP8.5). These productivity projections were applied within an otherwise uniform scenario (SSP2) and analyzed for their effect on total calorie demand, crop prices, and number of people at risk of undernourishment to quantify food security. Risks to climate change mitigation targets were explored by modeling the total bioenergy supply, emissions, and global mean temperature. The results revealed significant differences in the risk of food security and mitigation potential between different regions and climate change scenarios. Across scenarios, the crop area extent can vary up to 2 million km ^2 due to changing crop yields. The projected change in global hunger ranges from 60 to 160 million undernourished people, indicating uncertainty between climate and crop model combinations. Low-income regions are especially impacted because of their high sensitivity to changes in food prices. Global climate change mitigation ambitions can also deviate by the latter part of the 21st century, as changes in yields will impact biofuel production as well as agriculture, forestry and other land use emissions. The quantitative insights generated by this study highlight the need for global policy efforts to make the agricultural system more adaptive to climate change to handle potential negative impacts.

To achieve net zero carbon emissions by mid-century, the United States may need to rely on carbon dioxide removal (CDR) to offset emissions from difficult-to-decarbonize sectors and/or shortfalls in near-term mitigation efforts. CDR can be delivered using many approaches with different requirements for land, water, geologic carbon storage capacity, energy, and other resources. The availability of these resources varies by region in the U.S. suggesting that CDR deployment will be uneven across the country. Using the global change analysis model for the United States (GCAM-USA), we modeled six classes of CDR and explored their potential using four scenarios: a scenario where all the CDR pathways are available (Full Portfolio), a scenario with restricted carbon capture and storage (Low CCS), a scenario where the availability of bio-based CDR options is limited (Low Bio), and a scenario with constraints on enhanced rock weathering (ERW) capabilities (Low ERW). We find that by employing a diverse set of CDR approaches, the U.S. could remove between 1 and 1.9 GtCO _2 /yr by midcentury. In the Full Portfolio scenario, direct air carbon capture and storage (DACCS) predominates, delivering approximately 50% of CO _2 removal, with bioenergy with carbon capture and storage contributing 25%, and ERW delivering 11.5%. Texas and the agricultural Midwest lead in CDR deployment due to their abundant agricultural land and geological storage availability. In the Low CCS scenario, reliance on DACCS decreases, easing pressure on energy systems but increasing pressure on the land. In all cases CDR deployment was found to drive important impacts on energy, land, or materials supply chains (to supply ERW, for example) and these effects were generally more pronounced when fewer CDR technologies were available.

As the world seeks to increase ambition rapidly to limit global warming to 1.5 °C, joint leadership from the world's largest greenhouse gas (GHG) emitters—the United States (U.S.) and China—will be critical to deliver significant emissions reductions from their own countries as well as to catalyze increased international action. After a period of uncertainty in international climate policy, these countries now both have current leadership that supports ambitious climate action. In this context, a feasible, high-impact, and potentially globally catalytic agreement by the U.S. and China to transition away from coal to clean energy would be a major contribution toward this global effort. We undertake a plant-by-plant assessment in the power sector to identify practical coal retirement pathways for each country that are in line with national priorities and the global 1.5 °C target. Our plant-by-plant analysis shows that the 1.5 °C-compatible pathways may result in an average retirement age of 47 years for the U.S. coal plants and 22 years for Chinese coal plants, raising important questions of how to compare broader economic, employment, and social impacts. We also demonstrate that such pathways would also lead to significant emissions reductions, lowering overall global energy-related CO2 emissions by about 9% in 2030 relative to 2020. A catalytic effect from the possibility of other countries taking compatible actions is estimated to reduce global emissions by 5.1 Gt CO2 in 2030 and by 10.1 Gt CO2 in 2045.

The 17 Sustainable Development Goals (SDGs) and 169 targets proposed in 2015 are wide ranging and achieving them before 2030 may require extraordinarily high costs. Prioritizing a more manageable and logical sequence of SDGs targets based on national conditions is critical to reduce the complexity of SDGs, lower costs, ensure transitions are efficient, and accelerate implementation. Researchers have proposed a range of methods to rank the prioritizations of SDGs from different perspectives. Unfortunately, prioritizations of SDGs arising from different methods are not entirely consistent due to the limitations of each method. Therefore, an integrated methodological framework is required to reconcile these inconsistencies. To fill this research gap, we synthesized several methods to create a new composite assessment framework to prioritize SDGs targets. The framework consists of assessment models from three perspectives, including the impact of targets in a network composed of the interactions between targets, the gap between the targets' current and ideal performances, and the urgency of improving participation by government and society in achieving the targets. We then tested the effectiveness of this assessment framework empirically by ranking prioritizations for six targets of SDG 6 (clean water and sanitation) in Southeast Asia. Empirical results show that target 6.5 has the highest priority, followed by targets 6.4 and 6.6, while the lowest ranking target is 6.1. Finally, we outlined the advantages and limitations of each assessment method to assist stakeholders in using and broadening this composite assessment framework in the future. [ABSTRACT FROM AUTHOR]

This study investigated how subsurface and atmospheric leakage from geologic CO2 storage reservoirs could impact the deployment of Carbon Capture and Storage (CCS) in the global energy system. The Leakage Risk Monetization Model was used to estimate the costs of leakage for representative CO2 injection scenarios, and these costs were incorporated into the Global Change Assessment Model. Worst-case scenarios of CO2 leakage risk, which assume that all leakage pathway permeabilities are extremely high, were simulated. Even with this extreme assumption, the associated costs of monitoring, treatment, containment, and remediation resulted in minor shifts in the global energy system. For example, the reduction in CCS deployment in the electricity sector was 3% for the “high” leakage scenario, with replacement coming from fossil fuel and biomass without CCS, nuclear power, and renewable energy. In other words, the impact on CCS deployment under a realistic leakage scenario is likely to be negligible. We also quantified how the resulting shifts will impact atmospheric CO2 concentrations. Under a carbon tax that achieves an atmospheric CO2 concentration of 480 ppm in 2100, technology shifts due to leakage costs would increase this concentration by less than 5 ppm. It is important to emphasize that this increase does not result from leaked CO2 that reaches the land surface, which is minimal due to secondary trapping in geologic strata above the storage reservoir. The overall conclusion is that leakage risks and associated costs will likely not interfere with the effectiveness of policies for climate change mitigation.

The Indus River Basin (IRB) is a severely water-stressed and rapidly developing home to an estimated 250 million people in South Asia. An acute deficit of environmental flows (EFs) in the basin’s delta negatively impacts geomorphology and surrounding ecosystems. Here, a sub-national model of the IRB’s integrated water–energy–land systems is applied to quantify multi-sector transformations and system costs for enhancing EFs to the Indus Delta. The results show that increasing the average outflows from the basin relative to historical policy levels by 2.5 and 5 times would increase sectoral costs for upstream water users between 17–32 and 68–72% for low and high ecological potential targets. The enhanced EFs result in more energy for pumping and treating water upstream from the delta and a net increase in irrigation and energy investments. The EF policy costs are minimized by 7–14% through cooperation across countries and 6–9% through the coordinated implementation of water efficiency measures in the irrigation, conveyance, power plant cooling, and water treatment sectors. The results underscore the crucial role of a multi-sector, multi-scale collaboration in achieving EF targets in water-stressed river basins for ecosystem adaptation to climate vulnerability, restoration of the delta, and socio-economic benefits.

Hydrogen is increasingly recognized as a pivotal commodity in the low-carbon energy transition, bridging regional disparities in hydrogen production capacity and facilitating international energy trade. Existing research has yet to delve into the impact of global hydrogen trade on energy transitions, as well as the key factors driving the development of hydrogen trade. This study, utilizing the updated GCAM-TU model, which mainly extends the original Global Change Analysis Model (GCAM) with a hydrogen trade module, provides a multi-scenario outlook on the characteristics of hydrogen trade and its impact on global and regional energy transition. The findings indicate that by 2050, the global hydrogen trade volume is expected to reach 9-19EJ, accounting for a quarter of the global hydrogen consumption in different scenarios, with green hydrogen trade dominating due to its cost advantages. Pipeline hydrogen and ammonia shipping will govern the trade market, representing 33%–61% and 24%–64% of trade volume, respectively, while liquid hydrogen shipping holds an advantage for medium distances. Hydrogen trade is projected to increase global hydrogen demand by 1.2–5.3EJ and reduce hydrogen prices by 11%–36% in the European Union, Japan, and South Korea, thereby expanding their hydrogen consumption by 8%–55% and reducing regional carbon prices by 1%–6%. Moreover, hydrogen trade significantly alters the regional distribution of global hydrogen production, with major exporting regions such as North and East Africa increasing their hydrogen production for export by up to 150%. To facilitate the development of hydrogen and hydrogen trade, it is crucial for potential importing and exporting regions to enhance global financial and technological cooperation, develop key hydrogen production and transportation technologies, and retrofit existing nature gas infrastructure to reduce costs. Additionally, forming stable trade partnerships is essential for maintaining energy security in the hydrogen trade. [Display omitted] • A bilateral hydrogen trade module is incorporated into an integrated assessment model. • Hydrogen trade's impact on green transitions is evaluated, considering carbon constraints, hydrogen and transport costs. • By 2050, hydrogen trade will cover 25% of global demand, reshaping production distribution and aiding energy transitions. [ABSTRACT FROM AUTHOR]

Indus River Basin (IRB) region of South Asia is severely water-stressed with irrigation receiving 90–95% of total surface water allocations and depletion of fossil groundwater reserves of more than 30 km3/year. Simultaneously, many supply-driven hydropower reservoirs, are planned in the basin. The reservoirs constructed upstream inflict severe environmental damages and reduce water availability for irrigation downstream. Policymakers promote smart technologies as a demand-based solution to reduce water consumption in irrigation. However, the effects of such technologies are not yet well understood, and unintended consequences (such as irrigation efficiency paradox and other nexus externalities) have recently begun to appear. Therefore, we use an integrated assessment model to analyze the proliferation of smart technologies in the IRB. The analysis suggests that if the Indus countries adopt a demand-based approach and irrigate their land completely using smart technologies, surface and groundwater withdrawals are indeed reduced. However, this reduction comes with a 33% increase in total expenditures, an increase in consumption across water and energy sectors, and higher withdrawals from fossil groundwater reserves. On the other hand, we find that if the countries were to balance their investments between smart and hydropower technologies it would not only reduce the increment in expenditure to 28%, but would also conserve irrigation water while avoiding the increased multi-sectoral consumption and environmental degradation. Thus, balancing investments between smart irrigation and hydropower projects can significantly reduce the economic and environmental (including conservation of water resources, meeting environmental flow targets, among others) costs of multi-sector water conservation in the IRB. • This study utilized Integrated Assessment Model to analyze the proliferation of smart irrigation and hydropower technologies. • A purely demand-based (smart irrigation) approach increases expenditure while decreasing surface and groundwater withdrawals. • A purely supply-based (hydropower) approach increases surface and groundwater withdrawals. • Balancing between demand-and-supply based approaches together reduces expenditures and surface and groundwater withdrawals. • Balancing is considered a win-win strategy by delivering environmental benefits at a lower cost than individual strategy. [ABSTRACT FROM AUTHOR]

Future water scarcity is a global concern with impacts across the energy, water, and land (EWL) sectors. Countries in Latin America and the Caribbean (LAC) are significant producers of agricultural goods, so disruptions resulting from water scarcity in LAC have global importance. Understanding where water scarcity in LAC could occur and what could exacerbate it is critical for strategic resource management and planning, both regionally and globally. Assessing future water scarcity in LAC is challenging given the complex interactions among the EWL sectors and the multiple uncertainties acting across spatial scales. To illuminate these dynamics, we use scenario discovery on a large ensemble representing diverse futures simulated using an integrated human‐environmental systems model. We quantify future water scarcity and its economic impacts across several physical and economic metrics. We find that future levels of reservoir storage expansion could be a significant driver of physical and economic water scarcity, highlighting the importance of strategic water infrastructure development in maintaining future water availability. Changes in crop profit are driven by both water supply and demand, emphasizing the complexity of EWL multisector dynamics. While most of LAC is poised to have abundant land and water resources available for future development, basins in Mexico and along the Pacific coast of South America experience high exposure to severe outcomes and uncertainty across outcomes for at least one metric. We find that drivers of severe scarcity vary spatially and across metrics, highlighting the region's heterogeneity and the importance of considering multiple metrics to assess water scarcity. Key Points: The primary drivers of future water scarcity in Latin America and the Caribbean vary across space and among scarcity metricsBoth water supply and demand dynamics drive future severe water scarcity outcomes across Latin America and the CaribbeanScenarios of future water scarcity in the region reflect complex dynamics between local and global energy, water, and land systems [ABSTRACT FROM AUTHOR]

Abstract Future water scarcity is a global concern with impacts across the energy, water, and land (EWL) sectors. Countries in Latin America and the Caribbean (LAC) are significant producers of agricultural goods, so disruptions resulting from water scarcity in LAC have global importance. Understanding where water scarcity in LAC could occur and what could exacerbate it is critical for strategic resource management and planning, both regionally and globally. Assessing future water scarcity in LAC is challenging given the complex interactions among the EWL sectors and the multiple uncertainties acting across spatial scales. To illuminate these dynamics, we use scenario discovery on a large ensemble representing diverse futures simulated using an integrated human‐environmental systems model. We quantify future water scarcity and its economic impacts across several physical and economic metrics. We find that future levels of reservoir storage expansion could be a significant driver of physical and economic water scarcity, highlighting the importance of strategic water infrastructure development in maintaining future water availability. Changes in crop profit are driven by both water supply and demand, emphasizing the complexity of EWL multisector dynamics. While most of LAC is poised to have abundant land and water resources available for future development, basins in Mexico and along the Pacific coast of South America experience high exposure to severe outcomes and uncertainty across outcomes for at least one metric. We find that drivers of severe scarcity vary spatially and across metrics, highlighting the region's heterogeneity and the importance of considering multiple metrics to assess water scarcity.

Achieving sustainable development requires understanding how human behavior and the environment interact across spatial scales. In particular, knowing how to manage tradeoffs between the environment and the economy, or between one spatial scale and another, necessitates a modeling approach that allows these different components to interact. Existing integrated local and global analyses provide key insights, but often fail to capture ‘meso-scale’ phenomena that operate at scales between the local and the global, leading to erroneous predictions and a constrained scope of analysis. Meso-scale phenomena are difficult to model because of their complexity and computational challenges, where adding additional scales can increase model run-time exponentially. These additions, however, are necessary to make models that include sufficient detail for policy-makers to assess tradeoffs. Here, we synthesize research that explicitly includes meso-scale phenomena and assess where further efforts might be fruitful in improving our predictions and expanding the scope of questions that sustainability science can answer. We emphasize five categories of models relevant to sustainability science, including biophysical models, integrated assessment models, land-use change models, earth-economy models and spatial downscaling models. We outline the technical and methodological challenges present in these areas of research and discuss seven directions for future research that will improve coverage of meso-scale effects. Additionally, we provide a specific worked example that shows the challenges present, and possible solutions, for modeling meso-scale phenomena in integrated earth-economy models.

Net-zero greenhouse gas emission targets are central to current international efforts to stabilize global climate, and many of these plans rely on carbon dioxide removal (CDR) to meet mid-century goals. CDR can be performed via nature-based approaches, such as afforestation, or engineered approaches, such as direct air capture. Both will have large impacts in the regions where they are sited. We used the Global Change Analysis Model for the United States to analyze how regional resources will influence and be influenced by CDR deployment in service of United States national net-zero targets. Our modeling suggests that CDR will be deployed extensively, but unevenly, across the country. A number of US states have the resources, such as geologic carbon storage capacity and agricultural land, needed to become net exporters of negative emissions. But this will require reallocation of resources, such as natural gas and electricity, and dramatically increase water and fertilizer use in many places. Modeling these kinds of regional or sub-national impacts associated with CDR, as intrinsically uncertain as it is at this time, is critical for understanding its true potential in meeting decarbonization commitments.

Carbon-emitting technologies often cost less than carbon-emission-free alternatives; this difference in cost is known as the Green Premium. Innovations that decrease the Green Premium contribute to achieving climate goals, but a conceptual framework to quantify that contribution has been lacking. Here, we devise a framework to translate reductions in the Green Premium into equivalent reductions in carbon emissions. We introduce a new integrated assessment model designed for teaching and communication, the Climate Optimized INvestment model, to facilitate transparent investigation of cost-saving innovation. We look at consequences of introducing a new technology with potential for learning and improvement for scenarios with three levels of stringency of carbon constraint: an Unlimited budget scenario in which carbon emissions abatement is determined only by balancing marginal costs; a Large budget scenario with a maximum budget for future cumulative emissions equivalent to 50 times the initial-year emissions; and a Small budget scenario with a maximum budget for future cumulative emissions equivalent to 15 times the initial-year emissions. At all of these stringency levels, we find the least-cost solutions involve investing in a learning subsidy to bring the cost of the new technology down the learning curve. Reducing the Green Premium can lead to enhanced carbon abatement, lower abatement costs even after reaching net-zero emissions, less climate damage, and increased net-present-value of consumption. We find both the value of Green Premium reductions and the value of carbon dioxide removal are greater under more stringent mitigation targets. Our study suggests a crucial role for both public and private sectors in promoting and developing innovations that can contribute to achieving zero emissions goals.

Abstract In recent decades, grain self‐sufficiency has been a key security issue for certain countries, such as Russia, China, India, and France. Climate change may affect agricultural production and possibly influence the grain self‐sufficiency of some countries, threatening their food security. This article provides an updated integrated assessment of the impact of climate change on grain self‐sufficiency at the country level. The study links climatic variables projected by global climate models to economic variables simulated by a global multiregion multisector economic model. Although climate change may considerably reduce land productivity, our simulation results show that climate change in the long term modestly impacts regional grain self‐sufficiency, as the direct effects on yield are largely canceled out by changes in grain production via adaptation measures taken by producers in response to market signals. Behavioral changes of consumers also contribute modestly to cancel out the adverse impact on land productivity. In the RCP8.5 climate change scenario, the adverse impact of climate change on land productivity in 2100 may reach to 25% of the current level, but declines to 5% when changes in production and consumption are considered, thus indicating the role of long‐term adaptation by economic agents.

Abstract This study seeks to understand how Argentina's energy, water, and land (EWL) systems will co‐evolve under a representative array of human and earth system influences, including socioeconomic change, climate change, and climate policy. To capture Argentina's sub‐national EWL dynamics in the context of global change, we couple the Global Change Analysis Model with a suite of consistent, gridded sectoral downscaling models to explore multiple stakeholder‐engaged scenarios. Across scenarios, Argentina has the economic opportunity to use its vast land resources to satisfy growing domestic and international demand for crops, such as oil (e.g., soy) and biomass. The human (rather than earth) system produces the most dominant changes in mid‐century EWL resource use. A Reference scenario characterized by modest socioeconomic growth projects a 40% increase in Argentina's agricultural production by 2050 (relative to 2020) by using 50,000 km2 of additional cropland and 40% more water. A Climate Policy scenario designed to achieve net‐zero carbon emissions globally shortly after mid‐century projects that Argentina could use 100,000 km2 of additional land (and 65% more water) to grow biomass and other crops. The burden of navigating these national opportunities and challenges could fall disproportionately on a subset of Argentina's river basins. The Colorado and Negro basins could experience moderate‐to‐severe water scarcity as they simultaneously navigate substantial irrigated crop demand growth and climate‐induced declines in natural water availability. Argentina serves as a generalizable testbed to demonstrate that multi‐scale EWL planning challenges can be identified and managed more effectively via integrated analysis of coupled human‐earth systems.

Integrated energy-water-land (EWL) planning promotes synergies and avoids conflicts in ways that sector-specific planning approaches cannot. Many important decisions that influence emerging EWL nexus issues are implemented at regional (e.g., large river basin, electricity grid) and sub-regional (e.g., small river basin, irrigation district) scales. However, actual implementation of integrated planning at these scales has been limited. Simply collecting and visualizing data and interconnections across multiple sectors and sub-regions in a single modeling platform is a unique endeavor in many regions. This study introduces and applies a novel approach to linking together multiple sub-regions in a single platform to characterize and visualize EWL resource use, EWL system linkages within and among sub-regions, and the EWL nexus implications of future policies and investments. This integrated planning methodology is applied in the water-stressed Colorado River Basin in Argentina, which is facing increasing demands for agricultural and fossil fuel commodities. Guided by stakeholders, this study seeks to inform basin planning activities by characterizing and visualizing (1) the basin's current state of EWL resources, (2) the linkages between sectors within and among basin sub-regions, and (3) the EWL nexus implications of planned future agricultural development activities. Results show that water scarcity, driven in part by human demands that have historically reached 60% of total surface water supply, poses a substantial constraint to economic development in the basin. The Colorado basin has the potential to serve as a testbed for crafting novel and generalizable sub-regional EWL planning approaches capable of informing the EWL planning dialogue globally. [ABSTRACT FROM AUTHOR]

We show that economic models of climate change produce climate dynamics inconsistent with current climate science models: (i) the delay between CO2 emissions and warming is much too long and (ii) positive carbon cycle feedbacks are mostly absent. These inconsistencies lead to biased economic policy advice. Controlling for how the economy is represented, different climate models result in significantly different optimal CO2 emissions. A long delay between emissions and warming leads to optimal carbon prices that are too low and attaches too much importance to the discount rate. Similarly we find that omitting positive carbon cycle feedbacks leads to optimal carbon prices that are too low. We conclude that it is important for policy purposes to bring economic models in line with the state of the art in climate science, and we make practical suggestions for how to do so. [ABSTRACT FROM AUTHOR]

This study seeks to understand how Argentina's energy, water, and land (EWL) systems will co‐evolve under a representative array of human and earth system influences, including socioeconomic change, climate change, and climate policy. To capture Argentina's sub‐national EWL dynamics in the context of global change, we couple the Global Change Analysis Model with a suite of consistent, gridded sectoral downscaling models to explore multiple stakeholder‐engaged scenarios. Across scenarios, Argentina has the economic opportunity to use its vast land resources to satisfy growing domestic and international demand for crops, such as oil (e.g., soy) and biomass. The human (rather than earth) system produces the most dominant changes in mid‐century EWL resource use. A Reference scenario characterized by modest socioeconomic growth projects a 40% increase in Argentina's agricultural production by 2050 (relative to 2020) by using 50,000 km2 of additional cropland and 40% more water. A Climate Policy scenario designed to achieve net‐zero carbon emissions globally shortly after mid‐century projects that Argentina could use 100,000 km2 of additional land (and 65% more water) to grow biomass and other crops. The burden of navigating these national opportunities and challenges could fall disproportionately on a subset of Argentina's river basins. The Colorado and Negro basins could experience moderate‐to‐severe water scarcity as they simultaneously navigate substantial irrigated crop demand growth and climate‐induced declines in natural water availability. Argentina serves as a generalizable testbed to demonstrate that multi‐scale EWL planning challenges can be identified and managed more effectively via integrated analysis of coupled human‐earth systems. Plain Language Summary: Many countries are undertaking ambitious national efforts to address societal priorities such as economic growth, sustainable development goals, and mid‐century strategies to address climate change. Understanding how energy, water, and land (EWL) systems may interact with one another and these strategies is advantageous for national planning, particularly for identifying synergistic interventions (e.g., investments in infrastructure, policies, and institutions). Through a stakeholder‐engaged study in Argentina, we demonstrate that the strategic value of such national analysis can be enhanced by accounting for (a) the interrelationships among the EWL sectors; (b) global forces such as socioeconomic, climate, and policy change, which have strong relevance for trade‐oriented economies like Argentina's; and (c) the nature of EWL change sub‐nationally, where many important planning decisions are made that collectively determine the success of national priorities. Results show that global change creates opportunities for Argentina to use its vast land resources to supply domestic and international demands for various crops. However, the burden of achieving this national opportunity could fall disproportionately on a subset of river basins within Argentina, including the Colorado and Negro basins. The methodology and resulting insights we present here are broadly applicable to other regions facing similar challenges, particularly in Latin America. Key Points: Socioeconomic, climate, and policy change could increase domestic and international demand for crops in Argentina (e.g., oil and biomass)This economic opportunity poses planning challenges at the sub‐national level, where many important energy‐water‐land decisions are madeKey river basins may see increasing crop and water demands and declining natural water supply, suggesting a need for integrated planning [ABSTRACT FROM AUTHOR]

National energy models provide decarbonization strategies. Most national energy models focus on costs and greenhouse gas emissions only. However, this focus carries the risk that burdens shift to other environmental impacts. Energy models have therefore been extended by life-cycle assessment (LCA). Furthermore, deep decarbonization is only possible by targeting all high-emission sectors. Thus, we present a holistic national energy model that includes high-emission sectors and LCA. The model provides detailed environmental impacts for electricity, heat, and transport processes in Germany for meeting the climate targets up to 2050. Our results show that renewable energies and storage are key technologies for decarbonized energy systems. Furthermore, sector coupling is crucial and doubles electricity demand. Our LCA shows that environmental impacts shift from operation to infrastructure highlighting the importance of an impact assessment over the full life cycle. Decarbonization leads to many environmental cobenefits; however, it also increases freshwater ecotoxicity and depletion of metal and mineral resources. Thus, holistic planning of decarbonization strategies should also consider other environmental impacts.

Yangtze Economic Belt is one of the most dynamic regions in China in terms of population growth, economic progress, industrialization, and urbanization. It faces many resource constraints (food, energy) and environmental challenges (pollution, biodiversity loss) under rapid population growth and economic development. Interactions between human and natural systems are at the heart of the challenges facing the sustainable development of the Yangtze Economic Belt. Understanding these interactions poses challenges because human and natural systems evolve in response to a wide range of influences. Accounting for these complex dynamics requires a system tool that can represent the fundamental drivers of change and responses of the individual system as well as how different systems interact and co-evolve. By adopting the system thinking and the methodology of system dynamics simulation, an integrated assessment model for the Yangtze Economic Belt, named ANEMI_Yangtze, is developed based on the third version of the global integrated assessment model, ANEMI. Nine sectors of population, economy, land, food, energy, water, carbon, nutrients, and fish are currently included in ANEMI_Yangtze. This paper identifies the opportunities and challenges facing the Yangtze Economic Belt and presents the ANEMI_Yangtze model structure. It also includes: (i) the identification of the cross-sectoral interactions and feedbacks involved in shaping Yangtze Economic Belt's system behaviour over time; (ii) the identification of the feedbacks within each sector that drive the state variables in that sector; and (iii) the explanation of the theoretical and mathematical basis for those feedbacks. ANEMI_Yangtze was developed and calibrated sector by sector before coupling them together into complete ANEMI_Yangtze model. After the validation and robustness test, the ANEMI_Yangtze model can be used to support decision making, policy assessment, and scenario development. This study aims to improve the understanding of the complex interactions among human and natural systems in the Yangtze Economic Belt to provide foundation for science-based policies for the sustainable development of the economic belt. [ABSTRACT FROM AUTHOR]

Research on air quality and human health “co-benefits” from climate mitigation strategies represents a growing area of policy-relevant scholarship. Compared to other aspects of climate and energy policy evaluation, however, there are still relatively few of these co-benefits analyses. This sparsity reflects a historical disconnect between research quantifying energy and climate, and research dealing with air quality and health. The air quality co-benefits of climate, clean energy, and transportation electrification policies are typically assessed with models spanning social, physical, chemical, and biological systems. This review article summarizes studies to date and presents methods used for these interdisciplinary analyses. Studies in the peer-reviewed literature (n = 26) have evaluated carbon pricing, renewable portfolio standards, energy efficiency, renewable energy deployment, and clean transportation. A number of major findings have emerged from these studies: [1] decarbonization strategies can reduce air pollution disproportionally on the most polluted days; [2] renewable energy deployment and climate policies offer the highest health and economic benefits in regions with greater reliance on coal generation; [3] monetized air quality health co-benefits can offset costs of climate policy implementation; [4] monetized co-benefits typically exceed the levelized cost of electricity (LCOE) of renewable energies; [5] Electric vehicle (EV) adoption generally improves air quality on peak pollution days, but can result in ozone dis-benefits in urban centers due to the titration of ozone with nitrogen oxides. Drawing from these published studies, we review the state of knowledge on climate co-benefits to air quality and health, identifying opportunities for policy action and further research.

Biomass feedstocks can be used to substitute fossil fuels and effectively remove carbon from the atmosphere to offset residual CO2 emissions from fossil fuel combustion and other sectors. Both features make biomass valuable for climate change mitigation; therefore, CO2 emission mitigation leads to complex and dynamic interactions between the energy and the land-use sector via emission pricing policies and bioenergy markets. Projected bioenergy deployment depends on climate target stringency as well as assumptions about context variables such as technology development, energy and land markets as well as policies. This study investigates the intra- and intersectorial effects on physical quantities and prices by coupling models of the energy (REMIND) and land-use sector (MAgPIE) using an iterative soft-link approach. The model framework is used to investigate variations of a broad set of context variables, including the harmonized variations on bioenergy technologies of the 33rd model comparison study of the Stanford Energy Modeling Forum (EMF-33) on climate change mitigation and large scale bioenergy deployment. Results indicate that CO2 emission mitigation triggers strong decline of fossil fuel use and rapid growth of bioenergy deployment around midcentury (~ 150 EJ/year) reaching saturation towards end-of-century. Varying context variables leads to diverse changes on mid-century bioenergy markets and carbon pricing. For example, reducing the ability to exploit the carbon value of bioenergy increases bioenergy use to substitute fossil fuels, whereas limitations on bioenergy supply shift bioenergy use to conversion alternatives featuring higher carbon capture rates. Radical variations, like fully excluding all technologies that combine bioenergy use with carbon removal, lead to substantial intersectorial effects by increasing bioenergy demand and increased economic pressure on both sectors. More gradual variations like selective exclusion of advanced bioliquid technologies in the energy sector or changes in diets mostly lead to substantial intrasectorial reallocation effects. The results deepen our understanding of the land-energy nexus, and we discuss the importance of carefully choosing variations in sensitivity analyses to provide a balanced assessment. [ABSTRACT FROM AUTHOR]

We explore the implications of recent empirical findings about CO2 fertilization and climate sensitivity on the social cost of carbon (SCC) in the FUND model. New compilations of satellite and experimental evidence suggest larger agricultural productivity gains due to CO2 growth are being experienced than are reflected in FUND parameterization. We also discuss recent studies applying empirical constraints to the probability distribution of equilibrium climate sensitivity and we argue that previous Monte Carlo analyses in IAMs have not adequately reflected the findings of this literature. Updating the distributions of these parameters under varying discount rates is influential on SCC estimates. The lower bound of the social cost of carbon is likely negative and the upper bound is much lower than previously claimed, at least through the mid-twenty-first century. Also the choice of discount rate becomes much less important under the updated parameter distributions. [ABSTRACT FROM AUTHOR]

From charting out climate change mitigation pathways to estimating price risks associated with the social cost of carbon, as environmentally concerned citizens of the twenty-first century, we live in a culture of foresight. Because of a growing integration of an ever-wider sample space of possible climate futures into the present, historical experience has become seemingly irrelevant for effectively predicting where our climate transitions are headed, in effect restricting our sense of futurity to its performativity in the present. What has been surprisingly absent as a theoretical and methodological approach among sociologists, however, are treatments of the performativity of the future as the expression of a historical praxis for prognosis, with its own mode of disclosure. By interrogating the temporal structure of anticipation that characterizes computer-based simulations of emissions scenarios, the paper illustrates how this praxis discloses the future in accordance with the grammatical tense of the future perfect. It then argues that this relationship between past and future is the cultural product of a historically particular set of prognostic techniques and technologies, namely, model-based scenario analysis. Against this background, the paper seeks to contribute to the rehabilitation of the relevance of historical experience by historicizing the social ontological status of the future that theories of performativity take as their starting point. • When it comes to climate mitigation, our sense of futurity has become restricted to its performativity in the present. • This paper historicizes the ontological status of the future that theories of performativity take as their starting point. • It interrogates the temporal structure of anticipation that characterizes computer-based simulations of emissions scenarios. [ABSTRACT FROM AUTHOR]

As the energy transition accelerates and renewable energy technologies become cost-competitive with fossil fuels in many countries, the availability of finance could become a bottleneck. Integrated assessment models (IAMs) and other macro-economic transition (MET) models typically do not feature detailed financial markets and do not sufficiently consider financing barriers and opportunities for the transition to carbon neutrality. While progress has been made in the representation of financial markets in macro-models since the financial crisis of 2008 the focus has been on financial (in)stability of the financial sector, not its ability to finance investment projects in the energy transition. Hence, a crucial gap remains, preventing macro model-based analysis of financing barriers and policy interventions that may accelerate the energy transition. In this article we review how state-of-the-art macro-economic models consider the financial sector. From this review we identify what elements are still missing to adequately model the financial dynamics and challenges for the energy transition specifically. Based on a discussion of relevant parts of the finance literature, we then propose four steps to improve the representation of finance in global IAMs and MET models more generally.

Momentum to phase out unabated coal use is growing globally. This transition is critical to meeting the Paris climate goals but can potentially lead to large amounts of stranded assets, especially in regions with newer and growing coal fleets. Here we combine plant-level data with a global integrated assessment model to quantify changes in global stranded asset risks from coal-fired power plants across regions and over time. With new plant proposals, cancellations, and retirements over the past five years, global net committed emissions in 2030 from existing and planned coal plants declined by 3.3 GtCO _2 (25%). While these emissions are now roughly in line with initial Nationally Determined Contributions (NDCs) to the Paris Agreement, they remain far off track from longer-term climate goals. Progress made in 2021 towards no new coal can potentially avoid a 24% (503 GW) increase in capacity and a 55% ($520 billion) increase in stranded assets under 1.5 °C. Stranded asset risks fall disproportionately on emerging Asian economies with newer and growing coal fleets. Recent no new coal commitments from major coal financers can potentially reduce stranding of international investments by over 50%.