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Projection of land use and land-cover change is highly uncertain yet drives critical estimates of carbon emissions, climate change, and food and bioenergy production. We use new, spatially explicit land availability data in conjunction with a model sensitivity analysis to estimate the effects of additional land protection on land use and land cover. The land availability data include protected land and agricultural suitability and is incorporated into the Moirai land data system for initializing the Global Change Analysis Model. Overall, decreasing land availability is relatively inefficient at preserving undeveloped land while having considerable regional land-use impacts. Current amounts of protected area have little effect on land and crop production estimates, but including the spatial distribution of unsuitable (i.e., unavailable) land dramatically shifts bioenergy production from high northern latitudes to the rest of the world, compared with uniform availability. This highlights the importance of spatial heterogeneity in understanding and managing land change. Approximately doubling the current protected area to emulate a 30% protected area target may avoid land conversion by 2050 of less than half the newly protected extent while reducing bioenergy feedstock land by 10.4% and cropland and grazed pasture by over 3%. Regional bioenergy land may be reduced (increased) by up to 46% (36%), cropland reduced by up to 61%, pasture reduced by up to 100%, and harvested forest reduced by up to 35%. Only a few regions show notable gains in some undeveloped land types of up to 36%. Half of the regions can reach the target using only unsuitable land, which would minimize impacts on agriculture but may not meet conservation goals. Rather than focusing on an area target, a more robust approach may be to carefully select newly protected land to meet well-defined conservation goals while minimizing impacts to agriculture.

Background: Achieving a net zero greenhouse gas United States (US) economy is likely to require both deep sectoral mitigation and additional carbon dioxide removals to offset hard-to-abate emissions. Enhancing the terrestrial carbon sink, through practices such as the adoption of no-till and cover cropping agricultural management, could provide a portion of these required offsets. Changing domestic agricultural practices to optimize carbon content, however, might reduce or shift US agricultural commodity outputs and exports, with potential implications on respective global markets and land use patterns. Here, we use an integrated energy-economy-land-climate model to comprehensively assess the global land, trade, and emissions impacts of an adoption of domestic no-till farming and cover cropping practices based on carbon pricing. Results: We find that the adoption of these practices varies depending on which aspects of terrestrial carbon are valued. Valuation of all terrestrial carbon resulted in afforestation at the expense of domestic agricultural production. In contrast, a policy valuing soil carbon in agricultural systems specifically indicates strong adoption of no-till and cover cropping for key crops. Conclusions: We conclude that under targeted terrestrial carbon incentives, adoption of no-till and cover cropping practices in the US could increase the terrestrial carbon sink with limited effects on crop availability for food and fodder markets. Future work should consider integrated assessment modeling of non-CO2 greenhouse gas impacts, above ground carbon storage changes, and capital and operating cost considerations. [ABSTRACT FROM AUTHOR]

Deep learning-based anomaly detection in images has recently gained popularity as an investigative field with many global submissions. To simplify complex data analysis, researchers in the deep learning subfield of machine learning employ Artificial Neural Networks (ANNs) with many hidden layers. Finding data occurrences that significantly differ from generalizable to most data sets is the primary goal of anomaly detection. Many medical imaging applications use convolutional neural networks (CNNs) to examine anomalies automatically. While CNN structures are reliable feature extractors, they encounter challenges when simultaneously classifying and segmenting spots that need removal from scans. We suggest a separate and integration system to solve these issues, separated into two distinct departments: classification and segmentation. Initially, many network architectures are taught independently for each abnormality, and these networks’ main components are combined. A shared component of the branched structure functions for all abnormalities. The final structure has two branches: one has distinct sub-networks, each intended to classify a particular abnormality, and the other for segmenting various abnormalities. Deep CNNs training directly on high-resolution images necessitate input layer image compression, which results in the loss of information necessary for detecting medical abnormalities. A guided GradCAM (GCAM) tuned patch neural network is applied to full-size images for anomaly localization. Therefore, the suggested approach merges the pre-trained deep CNNs with class activation mappings and area suggestion systems to construct abnormality sensors and then fine-tunes the CNNs on picture patches, focusing on medical abnormalities instead of training on whole images. A mammogram data set was used to test the deep patch classifier, which had a 99% overall classification accuracy. A Brain tumor image data set was used to test the integrated detector’s ability to detect abnormalities, and it did so with an average precision of 0.99. [ABSTRACT FROM AUTHOR]

The urbanization process has an impact on greenhouse gas emissions, but little research has linked specific processes of urbanization to CO2 emissions. Particularly, for China, the urban–rural dual system and unique fertility policy play an important role in its urbanization process. A large population base and a sharp urban–rural divide need to be taken into account in CO2 emission forecast. Based on three urbanization processes corresponding to Shared Socioeconomic Pathways (SSPs) scenarios, this article combined the Population-Development-Environment (PDE) model and the Global Change Assessment Model (GCAM) to forecast energy consumption and CO2 emissions in China in this century. The results show as follows. 1. China’s CO2 emissions and energy consumption will experience an inverted U-shaped trend. In the rapid, moderate and slow urbanization scenarios, CO2 emissions peak in 2045, 2035 and 2030 at 11.55 billion tons, 10.88 billion tons and 10.65 billion tons, respectively. 2. The peak time of energy consumption is generally later than that of CO2 emissions. In the rapid, moderate and slow urbanization scenarios, the primary energy consumption peaks at 212.03 EJ in 2055, 190.65 EJ in 2055 and 171.80 EJ in 2045, respectively; the final energy consumption peaks at 192.34 EJ in 2055, 174.47 EJ in 2055 and 157.25 EJ in 2045, respectively. 3. More rapid urbanization corresponds to a later peak in CO2 emissions and energy consumption, but the decline is faster after the peak. In the future, China should vigorously implement intensive and sustainable policies to avoid excessive emissions in the urbanization process.

This paper reports on modelling outcomes for improvements to building energy performance in Indonesia. Long-term climate effects due to building energy demand and carbon emissions are also considered. The global change assessment model (GCAM) was used to generate the related end-user building energy data, including socioeconomics, for urban areas of Indonesia. As a comprehensive study, the total life cycle of carbon in the building sector and the concept of zero-carbon buildings, including energy efficiency, zero-emissions electricity and fuel-switching options, were considered. Building shell conductance (U-value) of the building envelope, floor area ratio (FAR), air conditioner (AC) efficiency, electrical appliance (APL) efficiency, rooftop photovoltaic (PV) performance and ground source heat pump (GSHP) systems were considered as parameters to mitigate carbon emissions under the operational energy category in the GCAM. Carbon mitigation associated with the cement production process was considered in the raw material category. Urban population and labour productivity in Indonesia were used as base inputs with projected growth rates to 2050 determined from the available literature. Low growth rate 'LowRate' and high growth rate 'HighRate' were considered as variable inputs for U-value, FAR, AC efficiency, APLs efficiency and PV capacity factor to model emissions mitigation. The energy consumption of the GSHP was compared to the conventional reverse cycle ACs to identify the potential of the GSHP as a fuel-switching option. In the GCAM, the benchmark (base case scenario) data set was generated based on input parameters (urban population and labour productivity rate) only for the residential building sector in Indonesia. Total potential carbon emissions mitigation was found to be 432 Mt CO2-e for the residential building sector in Indonesia over 2020–2050. It was found that an average of 24% carbon emissions mitigation could be achieved by 2020–2030 and 76% by 2031–2050. [ABSTRACT FROM AUTHOR]

The concept of sustainability inherently spans multiple spatial scales, sectors, variables, and time horizons. This study links a recently developed method of assessing present‐day agricultural sustainability across environmental, economic, and social dimensions with a process‐based integrated assessment model, in order to allow forward‐looking analysis of sustainability by region and scenario. The sustainable agriculture matrix estimates present‐day agricultural sustainability at the national level using 18 indicator variables, of which this study estimates nine to the year 2100, using an enhanced version of the Global Change Analysis Model. Scenarios include a reference scenario, and scenarios that apply the following measures, both individually and in combination, that are thought to improve sustainability: yield intensification, transition toward more plant‐based ("flexitarian") diets, and economy‐wide greenhouse gas emissions mitigation. The scenarios illustrate considerable complexity and tradeoffs inherent to efforts to improve agricultural sustainability in all regions globally. For example, yield intensification typically increases nitrogen pollution, flexitarian diets can reduce agricultural output, and greenhouse gas mitigation efforts may either increase deforestation or crowd out crop and livestock production due to consequent bioenergy demands. However, there is considerable inter‐regional heterogeneity in the responses, and the importance of such secondary responses also differs by region. The analysis and post‐processing methods developed in this study allow quantification and visualization of the absolute and relative magnitude of the tradeoffs between agricultural sustainability indicator variables across regions, time periods, and scenarios. Plain Language Summary: This study links two fundamentally different approaches of assessing long‐term agricultural sustainability. The first one, the sustainable agricultural matrix (SAM), uses 18 economic, environmental, and social indicator variables, available at the national scale for recent historical years. This approach characterizes present‐day sustainability, but isn't well‐suited to long‐term assessment in the context of changes in socio‐economic, technological, and/or environmental conditions. The second approach involves a process‐based integrated assessment model, GCAM, which explicitly tracks the physical inputs and outputs of the agricultural sector, and how these interact with the other dynamically evolving systems represented in the model (energy, water, land, atmosphere, climate). Because GCAM only carries information sufficient to calculate 3 of the 18 variables in the SAM, these approaches for assessing sustainability are normally quite distinct. In this study we expand the capabilities of GCAM in order to allow analysis of 9 of the 18 SAM variables, and perform an assessment of the global and regional consequences to 2100 of realizing several ambitions thought to improve sustainability in general: reducing greenhouse gas emissions, transitioning toward predominantly plant‐based diets, and intensifying agriculture so as to reduce the areal extent of cropland globally. Key Points: The sustainable agriculture matrix is estimated to 2100 in the Global Change Analysis ModelYield intensification, dietary shift, and greenhouse gas mitigation scenarios are assessedAssessment of these tradeoffs in a consistent framework improves the quality of information for decision‐making [ABSTRACT FROM AUTHOR]

Abstract The concept of sustainability inherently spans multiple spatial scales, sectors, variables, and time horizons. This study links a recently developed method of assessing present‐day agricultural sustainability across environmental, economic, and social dimensions with a process‐based integrated assessment model, in order to allow forward‐looking analysis of sustainability by region and scenario. The sustainable agriculture matrix estimates present‐day agricultural sustainability at the national level using 18 indicator variables, of which this study estimates nine to the year 2100, using an enhanced version of the Global Change Analysis Model. Scenarios include a reference scenario, and scenarios that apply the following measures, both individually and in combination, that are thought to improve sustainability: yield intensification, transition toward more plant‐based (“flexitarian”) diets, and economy‐wide greenhouse gas emissions mitigation. The scenarios illustrate considerable complexity and tradeoffs inherent to efforts to improve agricultural sustainability in all regions globally. For example, yield intensification typically increases nitrogen pollution, flexitarian diets can reduce agricultural output, and greenhouse gas mitigation efforts may either increase deforestation or crowd out crop and livestock production due to consequent bioenergy demands. However, there is considerable inter‐regional heterogeneity in the responses, and the importance of such secondary responses also differs by region. The analysis and post‐processing methods developed in this study allow quantification and visualization of the absolute and relative magnitude of the tradeoffs between agricultural sustainability indicator variables across regions, time periods, and scenarios.

Abstract Projection of land use and land‐cover change is highly uncertain yet drives critical estimates of carbon emissions, climate change, and food and bioenergy production. We use new, spatially explicit land availability data in conjunction with a model sensitivity analysis to estimate the effects of additional land protection on land use and land cover. The land availability data include protected land and agricultural suitability and is incorporated into the Moirai land data system for initializing the Global Change Analysis Model. Overall, decreasing land availability is relatively inefficient at preserving undeveloped land while having considerable regional land‐use impacts. Current amounts of protected area have little effect on land and crop production estimates, but including the spatial distribution of unsuitable (i.e., unavailable) land dramatically shifts bioenergy production from high northern latitudes to the rest of the world, compared with uniform availability. This highlights the importance of spatial heterogeneity in understanding and managing land change. Approximately doubling the current protected area to emulate a 30% protected area target may avoid land conversion by 2050 of less than half the newly protected extent while reducing bioenergy feedstock land by 10.4% and cropland and grazed pasture by over 3%. Regional bioenergy land may be reduced (increased) by up to 46% (36%), cropland reduced by up to 61%, pasture reduced by up to 100%, and harvested forest reduced by up to 35%. Only a few regions show notable gains in some undeveloped land types of up to 36%. Half of the regions can reach the target using only unsuitable land, which would minimize impacts on agriculture but may not meet conservation goals. Rather than focusing on an area target, a more robust approach may be to carefully select newly protected land to meet well‐defined conservation goals while minimizing impacts to agriculture.

To help meet its near-term NDC goals and long-term net-zero 2070 target, the Government of India has planned to establish a Carbon Credit Trading Scheme (CCTS), i.e. a domestic emission trading scheme (ETS). An ETS is an inherently cost-effective policy instrument for emission reduction, providing the greatest flexibility to reduce emissions from within and across sectors. An effective ETS requires design features that consider country-specific challenges and reflect its role within the larger policy package to achieve long-term emission reduction. Within the Indian context and in this study we therefore investigate—(i) what might be the role of the ETS in achieving India’s long-term mitigation targets? (ii) How might the various sectors interact under an emissions cap? (iii) How might the ETS interact with existing energy and climate policies? We do this analysis by running four main scenarios using the integrated assessment model GCAM (v6.0), adapted to India-specific assumptions and expectations. These scenarios are—(i) NZ (net-zero), (ii) NZ + ETS, (iii) NZ + CC (command and control), and (iv) NZ + RPO (renewables purchase obligations) + ETS. The NZ scenario assumes India’s near-term and long-term climate commitments of net zero by 2070. Scenarios with ETS (ii) and (iv) apply an emissions cap on four sectors—electricity, iron and steel, cement, and fertilizer. The scenario with CC applies a homogenous emission cap on each of the chosen sectors but does not allow cross-sectoral trading. The last scenario includes renewables purchase obligations (RPOs along with an ETS. We show that under a specific ETS emissions cap: (i) the electricity sector emerges as the largest source of cost-effective greenhouse gas (GHG) reduction options; (ii) ETS with trading across sectors is around 24% more cost-effective than ETS with trading only within sectors, (iii) RPOs can be complementary to an ETS although the impact of RPOs on GHG reductions in the electricity sector would need to be considered when setting the level of the ETS cap (or emissions intensity targets) or the RPO targets to avoid low carbon prices, and (iv) the direction and volume of financial transfers across sectors depends on allocation targets set by the government. Based on these results we provide design recommendations for India’s ETS.

Limiting global warming to under 2 °C would require stringent mitigation and likely additional carbon dioxide removal (CDR) to compensate for otherwise unabated emissions. Because of its technology readiness, relatively low cost, and potential co-benefits, the application of biochar to soils could be an effective CDR strategy. We use the Global Change Analysis Model, a global multisector model, to analyze biochar deployment in the context of energy system uses of biomass with CDR under different carbon price trajectories. We find that biochar can create an annual sink of up to 2.8 GtCO _2 per year, reducing global mean temperature increases by an additional 0.5%–1.8% across scenarios by 2100 for a given carbon price path. In our scenarios, biochar’s deployment is dependent on potential crop yield gains and application rates, and the competition for resources with other CDR measures. We find that biochar can serve as a competitive CDR strategy, especially at lower carbon prices when bioenergy with carbon capture and storage is not yet economical.

Future changes in climate and socioeconomic systems will exacerbate water scarcity. Previous studies on China’s water footprint and scarcity often consider only climate change or socioeconomic factors in isolation. Here, we address these issues by coupling an integrated assessment model, the Global Change Analysis Model, with a global hydrological model to project China’s future water footprints and water scarcity, considering both climate change and socioeconomic factors. We simulated China’s water footprints under 52 scenarios, which include four global climate models (GCMs) and 13 combinations of Shared Socioeconomic Pathway (SSP)–Representative Concentration Pathway (RCPs) scenarios. We then projected the intensity of water stress (WSI), defined as a ratio of water footprint to renewable water volume, based on the simulations of the SSP2-RCP6.0 scenario. Our results align well with statistical data on water footprint variations between 2005 and 2020. China’s water footprints are likely to peak around 2030 and then decrease. We find through a scenario matrix analysis that emission-mitigation measures will significantly impact the water footprint, particularly in the electricity sector, which will become the largest water use sector in the future. This means that the low carbon energy option on China’s path to carbon neutrality may aggravate water scarcity. Water stress in China is projected to be greatest in 2025–2035, and all northern basins will experience water scarcity. Projections based on all GCMs consistently show a decline in WSI in China after 2050.

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This paper reports on modelling outcomes for improvements to building energy performance in Indonesia. Long-term climate effects due to building energy demand and carbon emissions are also considered. The global change assessment model (GCAM) was used to generate the related end-user building energy data, including socioeconomics, for urban areas of Indonesia. As a comprehensive study, the total life cycle of carbon in the building sector and the concept of zero-carbon buildings, including energy efficiency, zero-emissions electricity and fuel-switching options, were considered. Building shell conductance (U-value) of the building envelope, floor area ratio (FAR), air conditioner (AC) efficiency, electrical appliance (APL) efficiency, rooftop photovoltaic (PV) performance and ground source heat pump (GSHP) systems were considered as parameters to mitigate carbon emissions under the operational energy category in the GCAM. Carbon mitigation associated with the cement production process was considered in the raw material category. Urban population and labour productivity in Indonesia were used as base inputs with projected growth rates to 2050 determined from the available literature. Low growth rate ‘LowRate’ and high growth rate ‘HighRate’ were considered as variable inputs for U-value, FAR, AC efficiency, APLs efficiency and PV capacity factor to model emissions mitigation. The energy consumption of the GSHP was compared to the conventional reverse cycle ACs to identify the potential of the GSHP as a fuel-switching option. In the GCAM, the benchmark (base case scenario) data set was generated based on input parameters (urban population and labour productivity rate) only for the residential building sector in Indonesia. Total potential carbon emissions mitigation was found to be 432 Mt CO2-e for the residential building sector in Indonesia over 2020–2050. It was found that an average of 24% carbon emissions mitigation could be achieved by 2020–2030 and 76% by 2031–2050.

Given the possible economic consequences, poorer countries have more challenges in delivering their Nationally Determined Contributions. As a developing country, Chile has pledged to attain carbon neutrality by 2050. While Chile has implemented several mitigation measures, it still relies heavily on carbon sequestration, intending to sequester around 65 MtCO 2 e by 2050. However, heavy reliance on sequestration poses several risks as the literature shows that natural sinks, particularly forest and land, are exposed to severe impacts from global warming and climate change. Fortunately, Chile has significant renewable energy potential, which, if fully utilized, may move the country towards a net negative emissions context. To assess if such a net-negative system is feasible in the context of Chile, a new regional version of the Global Change Analysis Model for Chile is developed. The model is used to investigate the effects and required levels of investment in renewable energy and decarbonization of end-use sectors to achieve economy-wide net negative emissions scenarios. The design of net negative pathways follows a statistical approach based on the expected sequestration capacity in 2050 and its corresponding confidence interval. The results are compared to scenarios that are aligned with the objective of carbon neutrality by 2050. The findings show that obtaining net-zero emissions by 2050 is possible, however achieving net negative systems will be dependent on existing sequestration capacity and the application of economic incentives to boost green energy deployment in Chile as well as to push such green energy, in the form of electricity or e-fuels, into hard to decarbonize final demand sectors, such as transport, mining, and industry demand sectors. The results also indicate that after significantly reducing CO 2 emissions from the energy sector (primarily the power sector), the agricultural sector and other urban and industrial sectors still contribute to non-significant levels of CH 4 and N 2 O emissions. • A new version of the GCAM-Chile Integrated Assessment model. • GCAM-Chile used to assess pathways towards net negative economy wide systems in 2050. • Pathways with reduced reliance on natural sources of carbon dioxide sequestration. • High levels of CH 4 and N 2 O emissions from agriculture, industrial and urban processes. • Significant increase in carbon prices are needed to reduced reliance on CO 2 sequestration. [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.

To meet the nationally determined contributions proposed by the countries that signed the Paris Agreement, investments must be made in renewable generation technologies such as solar and wind. However, due to their high variability, these technologies pose challenges in terms of meeting demand or generating excess electricity. For this reason, energy system models are designed to capture this variability by considering flexibility technologies. Nevertheless, it is important to note that some energy system models lack integration with other sectors. Therefore, integrated assessment models have been employed to evaluate mitigation strategies, as they endogenously consider the linkages between energy and non-energy sectors. In addition, due to their complexity, these models do not account for the variability of renewable resources. Hence, this research aims to address this issue. This work represents the first attempt to evaluate how the introduction of hourly resolution affects the outcomes of integrated assessment models, specifically focusing on the Global Change Analysis Model (GCAM). We employ a soft-linking approach between the GCAM and the Highway to Renewable Energy Systems model (H2RES, an hourly level energy system model) to accomplish this. The proposed approach is tested using Chile's Nationally Determined Contributions under different hydrological profiles in the power sector. The results show that it is possible to use the capacity obtained from the Global Change Analysis Model and implement it on an hourly scale. However, the feasibility of implementation depends on high levels of flexibility technologies, such as battery energy storage. When given the choice of investments in renewable sources and flexible technologies, the optimal dispatch of the H2RES model show small differences than those obtained by GCAM-Chile. H2RES differs from GCAM-Chile in approximately 5% for wind and 3% for solar electricity generation in the year 2050. However, feasible integration of significant renewable sources is obtained with relatively high Critical Excess Electricity Production levels, reaching 20% in 2050. This excess electricity is attributed to the necessity for flexible technologies to manage the intermittency of renewables sources when hourly profiles of such sources are considered. • Assessment of the importance of the hourly variability for carbon neutrality pathways. • Feasible integration of large renewable resources with high critical excess electricity production levels. • Large deployment of energy storage for integration of variable renewable energy. • Solar Photovoltaic, preferably over wind energy to compensate for dry – hydrology. [ABSTRACT FROM AUTHOR]

As a party to the Paris Agreement, Canada has an ambitious climate target of net-zero emissions by 2050. The country also holds the world's third largest oil reserves in the Alberta oil sands. Given increasing emissions from the oil sands sector, achieving Canada's net-zero target requires significant oil sands decarbonization. If, while phasing out fossil fuels, there is still a demand for Canadian oil sands, then the decarbonization of the resource production process becomes crucial. In this study, we use an enhanced version of the Global Change Analysis Model (GCAM) with a detailed unconventional oil sector for Canada, including mining and in situ resources. We ask, what is the future of the oil sands sector in deeply decarbonized global and Canadian economies? We address this question under four mitigation scenarios with varying global net-zero GHG emissions constraints, three additional representative lower carbon extraction technologies available for the oil sands sector, as well as global direct air capture (DAC) deployment. We find that lower carbon technology deployment allows a 20%–44% increase in oil sands production by 2050 for scenarios with net-zero GHG emissions in 2100 or 2075. DAC helps maintain oil sands production in the most ambitious global decarbonization scenario (net-zero GHG by 2050), without which low international oil demand makes Canadian oil sands production uncompetitive. Canadian oil sands production thus depends highly on the availability of lower carbon extraction technologies and international oil demand, which to a certain extent relies on the availability and global deployment of negative emissions technologies.

Understanding potential impacts of climate change is complicated by spatially mismatched land representations between gridded datasets and models, and land use models with larger regions defined by geopolitical and/or biophysical criteria. Here we quantify the sensitivity of Global Change Assessment Model (GCAM) outputs to the delineation of Agro-Ecological Zones (AEZs), which are normally based on historical (1961–1990) climate. We reconstruct GCAM's land regions using projected (2071–2100) climate, and find large differences in estimated future land use that correspond with differences in agricultural commodity prices and production volumes. Importantly, historically delineated AEZs experience spatially heterogeneous climate impacts over time, and do not necessarily provide more homogenous initial land productivity than projected AEZs. We conclude that non-climatic criteria for land use region delineation are likely preferable for modeling land use change in the context of climate change, and that uncertainty associated with land delineation needs to be quantified.

Understanding potential impacts of climate change is complicated by spatially mismatched land representations between gridded datasets and models, and land use models with larger regions defined by geopolitical and/or biophysical criteria. Here we quantify the sensitivity of Global Change Assessment Model (GCAM) outputs to the delineation of Agro-Ecological Zones (AEZs), which are normally based on historical (1961–1990) climate. We reconstruct GCAM's land regions using projected (2071–2100) climate, and find large differences in estimated future land use that correspond with differences in agricultural commodity prices and production volumes. Importantly, historically delineated AEZs experience spatially heterogeneous climate impacts over time, and do not necessarily provide more homogenous initial land productivity than projected AEZs. We conclude that non-climatic criteria for land use region delineation are likely preferable for modeling land use change in the context of climate change, and that uncertainty associated with land delineation needs to be quantified.

In the pursuit of carbon neutrality, China's 2060 targets have been largely anchored in reducing greenhouse gas emissions, with less emphasis on the consequential benefits for air quality and public health. This study pivots to this critical nexus, exploring how China's carbon neutrality aligns with the World Health Organization's air quality guidelines (WHO AQG) regarding fine particulate matter (PM 2.5 ) exposure. Coupling a technology-rich integrated assessment model, an emission-concentration response surface model, and exposure and health assessment, we find that decarbonization reduces sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), and PM 2.5 emissions by more than 90%; reduces nonmethane volatile organic compounds (NMVOCs) by more than 50%; and simultaneously reduces the disparities across regions. Critically, our analysis reveals that further targeted reductions in air pollutants, notably NH 3 and non-energy-related NMVOCs, could bring most Chinese cities into attainment of WHO AQG for PM 2.5 5 to 10 years earlier than the pathway focused solely on carbon neutrality. Thus, the integration of air pollution control measures into carbon neutrality strategies will present a significant opportunity for China to attain health and environmental equality.

[Display omitted] • Combined with PED model and GCAM, CO 2 emissions under different urbanization scenarios are predicted. • China's unique urbanization process is reflected – the urban–rural dual system and the birth policy. • China can reach its carbon peak by 2030 with slow urbanization. • The peak time of energy consumption is generally later than that of CO 2. • In the long run, more rapid urbanization has a stronger emission reduction effect. The urbanization process has an impact on greenhouse gas emissions, but little research has linked specific processes of urbanization to CO 2 emissions. Particularly, for China, the urban–rural dual system and unique fertility policy play an important role in its urbanization process. A large population base and a sharp urban–rural divide need to be taken into account in CO 2 emission forecast. Based on three urbanization processes corresponding to Shared Socioeconomic Pathways (SSPs) scenarios, this article combined the Population-Development-Environment (PDE) model and the Global Change Assessment Model (GCAM) to forecast energy consumption and CO 2 emissions in China in this century. The results show as follows. 1. China's CO 2 emissions and energy consumption will experience an inverted U-shaped trend. In the rapid, moderate and slow urbanization scenarios, CO 2 emissions peak in 2045, 2035 and 2030 at 11.55 billion tons, 10.88 billion tons and 10.65 billion tons, respectively. 2. The peak time of energy consumption is generally later than that of CO 2 emissions. In the rapid, moderate and slow urbanization scenarios, the primary energy consumption peaks at 212.03 EJ in 2055, 190.65 EJ in 2055 and 171.80 EJ in 2045, respectively; the final energy consumption peaks at 192.34 EJ in 2055, 174.47 EJ in 2055 and 157.25 EJ in 2045, respectively. 3. More rapid urbanization corresponds to a later peak in CO 2 emissions and energy consumption, but the decline is faster after the peak. In the future, China should vigorously implement intensive and sustainable policies to avoid excessive emissions in the urbanization process. [ABSTRACT FROM AUTHOR]

The Paris agreement called on parties to formulate long-term low greenhouse gas emission development strategies. This paper aims to contribute to the definition of Colombia's mid-century strategy. For this, we use the Global Change Analysis Model (GCAM) to develop three representative pathways toward deep decarbonization by mid-century. We explore what might happen under Colombia's current policy trajectory, and in the context of 30% and 90% reductions in CO2 emissions by mid-century. The scenarios are intended to provide insights into strategic issues at the heart of long-term climate policy planning. We find that current and announced policies do not lead to net-zero emissions and that decarbonization requires early and sustained efforts toward clean energy production. We also find that stopping deforestation and intensifying agriculture are key components in any effort to decarbonize and for improving livelihoods. The use of emerging technologies such as sustainable bio-based fuels, the electrification of the transport fleet and the massive deployment of carbon free power generation will play a crucial role in decarbonization.

Metis was developed to allow users to analyze regional and sub-regional multi-sector dynamics by providing a platform to harmonize and amalgamate data from different models and stakeholders operating at variable spatial scales. Metis is an open-source R package hosted on GitHub. Metis functions collectively allow users to compare, manipulate, and harmonize multi-sector data at user-specified spatial scales, and to identify and quantify sectoral inter-linkages. Each Metis function can also be used independently to support an array of other research applications, such as spatial analysis and data visualization. Funding statement: This research was supported by the U.S. Department of Energy, Office of Science, as part of research in Multi-Sector Dynamics, Earth and Environmental System Modeling Program. The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830.

Metis was developed to allow users to analyze regional and sub-regional multi-sector dynamics by providing a platform to harmonize and amalgamate data from different models and stakeholders operating at variable spatial scales. Metis is an open-source R package hosted on GitHub. Metis functions collectively allow users to compare, manipulate, and harmonize multi-sector data at user-specified spatial scales, and to identify and quantify sectoral inter-linkages. Each Metis function can also be used independently to support an array of other research applications, such as spatial analysis and data visualization. [ABSTRACT FROM AUTHOR]

Most studies assessing climate impacts on agriculture have focused on average changes in market-mediated responses (e.g. changes in land use, production, and consumption). However, the response of global agricultural markets to interannual variability (IAV) in climate and biophysical shocks is poorly understood and not well represented in global economic models. Here we show a strong transmission of IAVs in climate-induced biophysical yield shocks to agriculture markets, which is further magnified by endogenous market fluctuations generated due to producers’ imperfect expectations of market and weather conditions. We demonstrate that the volatility of crop prices and consumption could be significantly underestimated (i.e. on average by 55% and 41%, respectively) by assuming perfect foresight, a standard assumption in the economic equilibrium modeling, compared with the relatively more realistic adaptive expectations. We also find heterogeneity in IAV across crops and regions, which is considerably mediated by international trade. Studying IAV provides fundamentally new insights on measuring and understanding climate impacts on global agriculture, and our framework lays the foundation for further investigating the full range of climate impacts on biophysical and human systems.

Studies exploring long-term energy system transitions rely on resource cost-supply curves derived from estimates of renewable energy (RE) potentials to generate wind and solar power projections. However, estimates of RE potentials are characterized by large uncertainties stemming from methodological assumptions that vary across studies, including factors such as the suitability of land and the performance and configuration of technology. Based on a synthesis of modeling approaches and parameter values used in prior studies, we explore the implications of these uncertain assumptions for onshore wind and solar photovoltaic electricity generation projections globally using the Global Change Analysis Model. We show that variability in parametric assumptions related to land use (e.g. land suitability) are responsible for the most substantial uncertainty in both wind and solar generation projections. Additionally, assumptions about the average turbine installation density and turbine technology are responsible for substantial uncertainty in wind generation projections. Under scenarios that account for climate impacts on wind and solar energy, we find that these parametric uncertainties are far more significant than those emerging from differences in climate models and scenarios in a global assessment, but uncertainty surrounding climate impacts (across models and scenarios) have significant effects regionally, especially for wind. Our analysis suggests the need for studies focusing on long-term energy system transitions to account for this uncertainty.

Future changes in climate and socioeconomic systems will drive both the availability and use of water resources, leading to evolutions in scarcity. The contributions of both systems can be quantified individually to understand the impacts around the world, but also combined to explore how the coevolution of energy-water-land systems affects not only the driver behind water scarcity changes, but how human and climate systems interact in tandem to alter water scarcity. Here we investigate the relative contributions of climate and socioeconomic systems on water scarcity under the Shared Socioeconomic Pathways-Representative Concentration Pathways framework. While human systems dominate changes in water scarcity independent of socioeconomic or climate future, the sign of these changes depend particularly on the socioeconomic scenario. Under specific socioeconomic futures, human-driven water scarcity reductions occur in up to 44% of the global land area by the end of the century.

The Shared Socioeconomic Pathways (SSPs) were developed without explicit assumptions for the future of the water sector; therefore, projections of future water demands based on the SSPs often lack a treatment of water technology assumptions that is consistent with the SSP storylines. This study has developed a set of qualitative and quantitative assumptions for future water sector technological advancements in the agricultural, electricity, manufacturing, and municipal sectors within the SSPs and then applied the resulting scenarios to an integrated assessment model to permit analysis of future water demand in a water‐constrained world. These scenarios are then compared to another set that excludes the adoption of water‐efficient technologies. Water demand impacts of individual SSP assumption categories are analyzed to determine scenario‐by‐scenario changes. By 2100, global annual water demands range from 3,560 to 6,600 km3. The results show that (1) technological change in the water sector can act to reduce water demand in a water limited world by up to 32% in 2100 in the SSP scenarios, (2) the most sustainable scenario produces end‐of‐century water withdrawals lower than 2010 values, (3) low‐income regions will likely be one of the largest drivers of future water demands and exhibit the greatest sensitivity to highly‐efficient water technologies, and (4) nonwater sector SSP assumptions have significant and differing impacts on demands across SSP scenarios that act to alter global water demands. Key Points: Shared Socioeconomic Pathway qualitative and quantitative model assumptions are expanded to include the water sectorFuture global water demand for all five SSP scenarios is analyzed with and without technological advancements in the water sectorWhere water supplies are constrained, global water demand reductions of up to 32% are possible in SSP scenarios by 2100 [ABSTRACT FROM AUTHOR]

Land-use changes under the shared socioeconomic pathways (SSPs) and the representative concentration pathways (RCPs) have been analyzed globally, but how regional and national land use respond to the global mitigation policies is seldom explored, which poses difficulties in regional environmental adaptation and decision-making. China, as a major food consuming and biofuel production country, would suffer great uncertainties in future land-use dynamics under the global scenarios. Here, we present a scenario-based land-use change assessment framework, integrating Global Change Assessment Model and Future Land Use Simulation Model, to evaluate the potential land use projections of China from 2010 to 2100. Eight scenarios with different combinations of SSPs and radiative forcing targets of RCPs are designed, to analyze the impacts of the global socioeconomic and emission assumptions on regional mitigations and land-use changes. We recalibrated the historical land use data and urban dynamics of China to improve the consistency of modeling results with the actual regional changes. Meanwhile, differences in land use dynamics are demonstrated by spatial downscaling, which are jointly affected by the global assumptions and local driving factors, showing a fierce competition between the crop and forest. We find that the regional crop changes are sensitive to the socioeconomic dynamics as well as the bioenergy production, while different carbon regimes drive the forest changes in unexpected ways. Besides, overall heterogeneous landscape patterns and similar spatial suitability maps are found in distributions of land-use change between the emission and socioeconomic scenarios. The results indicate that this framework embedded with the consideration of anthropogenic managements as well as the detailed interactions of local environments provides an effective way to investigate regional land use response to a range of alternative future pathways. [ABSTRACT FROM AUTHOR]

In this study, GCAM-China is exercised to examine the impact of CCS availability on the projected evolution of China's electric power sector under the Paris Increased Ambition policy scenario developed by Fawcett et al. based on the Intended Nationally Determined Contributions (INDCs) submitted under the COP-21 Paris Agreement. This policy scenario provides a backdrop for understanding China's electric generation mix over the coming century under several CCS availability scenarios. In all scenarios, the electric power sector shifts towards low-carbon generation technologies including significant nuclear, wind, and solar to meet growing demands and emissions targets. The availability and timing of CCS technologies to deploy at scale impacts the resulting generation mix and mitigation costs. Should large-scale CCS deployment be delayed in China by 25 years, the modeled per-ton cost of climate change mitigation is projected to be roughly $420/tC (2010 US dollars) by 2050, relative to $360/tC in the case in which CCS is available to deploy by 2025, a 16% increase. Once CCS is available for commercial use, mitigation costs for the two cases converge, equilibrating by 2085. However, should CCS be entirely unavailable to deploy in China, the mitigation cost spread, compared to the 2025 case, doubles by 2075 ($580/tC and $1130/tC respectively), and triples by 2100 ($1050/tC vs. $3200/tC). However, while delays in CCS availability may have short-term impacts on China's overall per-ton cost of meeting the emissions reduction target evaluated here, the net impact is much smaller compared with not having CCS available within the century and in each case the carbon price is likely to approach the price path associated with the full CCS availability case within a decade following CCS deployment. Having CCS available before the end of the century, even under the delays examined here, could reduce the total amount of nuclear and renewable energy that must deploy, significantly reducing the overall cost of meeting the emissions mitigation targets. [ABSTRACT FROM AUTHOR]

This paper describes preliminary results of analysis using the Global Change Assessment Model (GCAM) to evaluate the potential role of CCS in achieving emissions reduction targets. Scenarios are modelled using the Paris-Increased Ambition (PIA) case developed by Fawcett et al. (2015), and a more aggressive Paris Two-Degree Ambition (P2A) case. Both cases are based upon nationally determined contributions (NDCs) agreed to at the UNFCCC Conference of Parties (COP-21) in December 2015, coupled with additional mitigation effort beyond the 2030 Paris timeframe, through the end of the century. Analysis of CCS deployment and abatement costs under both policy scenarios suggests that, as modelled, having CCS in the technological portfolio could reduce the global cost of addressing emissions reduction targets specified under the policy scenario by trillions of dollars. Through the end of the century, total global abatement costs over the century associated with the PIA case – with five percent annual reduction in emission intensity and reaching 2.2 degrees by 2100 – are reduced by $15 trillion USD in the scenario where CCS is available to deploy by 2025 and remains available through 2100, nearly halving the cost of climate change abatement. Under the more ambitious P2A case, with 8 percent annual reduction in emission intensity and reaching 1.9 degrees by 2100, the availability of CCS reduces global abatement costs by $22 trillion USD through the end of the century, again nearly halving the costs of addressing the policy, relative to achieving the same target using an energy portfolio that does not include CCS. PIA and P2A scenarios with CCS result in 1,250 and 1,580 GtCO 2 of global geologic storage through the end of the century, respectively. [ABSTRACT FROM AUTHOR]

In this study, we use an integrated assessment model – GCAM, whose results were included in the IPCC Fifth Assessment Report – to explore the ways in which CCS could be used in a future carbon-constrained world. We focus on possible roles for CCS applications across different sectors – electricity, liquid fuels (predominantly, biofuels for transportation), and industry – and coupled to different primary fuels (oil, gas, coal, and biomass). To generate scenarios, we assume an increasing economy-wide global price on GHG emissions high enough to match several prescribed radiative forcing targets, approximating a least-cost mitigation pathway for a given target. Results show that the deployment of CCS technologies is not limited to fossil fuels, nor to power plants. There is potential for significant long-term climate change mitigation from application of CCS in the use of biomass to produce both electricity and liquid fuels. Moreover, in many climate change mitigation scenarios examined with GCAM, most biofuels and bio-electricity over the 21 st century use CCS to reduce their emissions. These results can be explained in terms of the relative cost competition between technologies in GCAM. [ABSTRACT FROM AUTHOR]

We report results of a 'hindcast' experiment focusing on the agricultural and land-use component of the Global Change Assessment Model (GCAM). We initialize GCAM to reproduce observed agriculture and land use in 1990 and forecast agriculture and land use patterns on one-year time steps to 2010. We report overall model performance for nine crops in 14 regions. We report areas where the hindcast is in relatively good agreement with observations and areas where the correspondence is poorer. We find that when given observed crop yields as input data, producers in GCAM implicitly have perfect foresight for yields leading to over compensation for year-to-year yield variation. We explore a simple model in which planting decisions are based on expectations but production depends on actual yields and find that this addresses the implicit perfect foresight problem. Second, while existing policies are implicitly calibrated into IAMs, changes in those policies over the period of analysis can have a dramatic effect on the fidelity of model output. Third, we demonstrate that IAMs can employ techniques similar to those used by the climate modeling community to evaluate model skill. We find that hindcasting has the potential to yield substantial benefits to the IAM community. [ABSTRACT FROM AUTHOR]

Understanding potential impacts of climate change is complicated by spatially mismatched land representations between gridded datasets and models, and land use models with larger regions defined by geopolitical and/or biophysical criteria. Here we quantify the sensitivity of Global Change Assessment Model (GCAM) outputs to the delineation of Agro-Ecological Zones (AEZs), which are normally based on historical (1961–1990) climate. We reconstruct GCAM's land regions using projected (2071–2100) climate, and find large differences in estimated future land use that correspond with differences in agricultural commodity prices and production volumes. Importantly, historically delineated AEZs experience spatially heterogeneous climate impacts over time, and do not necessarily provide more homogenous initial land productivity than projected AEZs. We conclude that non-climatic criteria for land use region delineation are likely preferable for modeling land use change in the context of climate change, and that uncertainty associated with land delineation needs to be quantified. [ABSTRACT FROM AUTHOR]

Implementing nationally determined contributions is more challenging for developing countries given potential economic consequences. Chile, a developing economy, is committed to reaching carbon neutrality by 2050. To do so, Chile announced multiple mitigation strategies such as phasing out coal by 2040, peaking emissions by 2025, and developing renewable energies. Fortunately, Chile holds a prominent renewable potential, standing out for solar, but it also has a significant challenge decarbonizing an economy that heavily relies on fossil fuels. The contribution of this paper is twofold. First, the first country-level disaggregated version of GCAM Latin America (GCAM-LA) was developed, where all South American countries are modeled as an independent energy-economy region. This model includes Chile as a separated region and incorporates interactions among the energy, water, agriculture and land use, economy, and climate systems. Second, different decarbonization strategies to reach carbon neutrality by 2050 were obtained, considering technology availability and high renewable energy potential. Results indicate that carbon neutrality is feasible when enforcing different combinations of the current Chilean mitigation strategies, even delaying coal phase-out by five years and failure of developing advanced solar technologies. If some policies are not fully implemented, such as delaying coal phase-out by five years or failure of developing advanced solar technologies, carbon neutrality can be achieved by incurring in a higher capital cost in the power sector. Moreover, decarbonization is mainly driven by high electrification levels in the final demand sector, reaching 53.7%–62.9% of the total consumption. However, such levels of electrification are reduced, particularly in the transport sector, when Chile relies on negative emissions from the land use and forestry sector. [ABSTRACT FROM AUTHOR]