Unlocking the potential of forage fish to reduce the global burden of disease.

Red meat consumption is associated with an elevated risk of mortality from non-communicable diseases (NCDs). In contrast, forage fish, as highly nutritious, environmentally friendly, affordable, and the most abundant fish species in the ocean, are receiving increasing interest from a global food system perspective. However, little research has examined the impact of replacing red meat with forage fish in the global diet on diet-related NCDs. METHODS: We based our study on datasets of red meat projections in 2050 for 137 countries and forage fish catches. We replaced the red meat consumption in each country with forage fish (from marine habitats), without exceeding the potential supply of forage fish. We used a comparative risk assessment framework to investigate how such substitutions could reduce the global burden of diet-related NCDs in adults. RESULTS: The results of our study show that forage fish may replace only a fraction (approximately 8%) of the world's red meat due to its limited supply, but it may increase global daily per capita fish consumption close to the recommended level. Such a substitution could avoid 0.5-0.75 million deaths and 8-15 million disability-adjusted life years, concentrated in low- and middle-income countries. Forage fish as an alternative to red meat could double (or more) the number of deaths that could be avoided by simply reducing red meat consumption. CONCLUSIONS: Our analysis suggests that forage fish is a promising alternative to red meat. Policies targeting the allocation of forage fish to regions where they are needed, such as the Global South, could be more effective in maximising the potential of forage fish to reduce the global burden of disease.

How climate change may shift power demand in Japan: Insights from data-driven analysis.

The impact of climate change on power demand in Japan and its related CO2 emissions is a matter of concern for the Japanese authorities and power companies as it may have consequences on the power grid, but is also of global importance as Japan is a significant contributor to global greenhouse gas emissions. In this study, we trained random forest models against daily power data in ten Japanese regions and for different types of power generation to project changes in future power production and its carbon intensity. We used climate variables, heat stress indices, and one variable for the level of human activities. We then used the models trained from the present-day period to estimate the future power demand, carbon intensity, and pertaining CO2 emissions over the period 2020-2100 under three Shared Socioeconomic Pathways (SSPs) scenarios (SSP126, SSP370, and SSP585). The impact of climate change on CO2 emissions via power generation shows seasonal and regional disparities. In cold regions, a decrease in power demand during winter under future warming leads to an overall decrease in power demand over the year. In contrast, the decrease in winter power demand in hot regions can be overcompensated by an increase in summer power demand due to more frequent hot days, resulting in an overall annual increase. From our regional models, power demand is projected to increase the most in most Japanese regions in May, June, September, and October rather than in the middle of summer, as found in previous studies. This increase could result in regular power outages during those months as the power grid could become particularly tense. Overall, we observed that power demand in regions with extreme climates is more sensitive to global warming than in temperate regions. The impact of climate change on power demand induces a net annual decrease in CO2 emissions in all regions except for Okinawa, in which power demand strongly increases during the summer, resulting in a net annual increase in CO2 emissions. However, climate change's impact on carbon intensity may reverse the trend in some regions (Shikoku, Tohoku). Additionally, we assessed the relative impacts of socioeconomic factors such as population, GDP, and environmental policies on CO2 emissions. When combined with these factors, we found that the climate change effect is more important than when considered individually and significantly impacts total CO2 emissions under SSP585. The contrasting results observed in the warm and cold regions of Japan can offer valuable insight into the potential future variations in energy demand and resulting CO2 emissions on a global scale.

Estimating impact likelihoods from probabilistic projections of climate and socio-economic change using impact response surfaces.

Estimates of future climate change impacts using numerical impact models are commonly based on a limited selection of projections of climate and other key drivers. However, the availability of large ensembles of such projections offers an opportunity to estimate impact responses probabilistically. This study demonstrates an approach that combines model-based impact response surfaces (IRSs) with probabilistic projections of climate change and population to estimate the likelihood of exceeding pre-specified thresholds of impact. The changing likelihood of exceeding impact thresholds during the 21st century was estimated for selected indicators in three European case study regions (Iberian Peninsula, Scotland and Hungary), comparing simulations that incorporate adaptation to those without adaptation. The results showed high likelihoods of increases in heat-related human mortality and of yield decreases for some crops, whereas a decrease of NPP was estimated to be exceptionally unlikely. For a water reservoir in a Portuguese catchment, increased likelihoods of severe water scarce conditions were estimated for the current rice cultivation. Switching from rice to other crops with lower irrigation demand changes production risks, allowing for expansion of the irrigated areas but introducing a stronger sensitivity to changes in rainfall. The IRS-based risk assessment shown in this paper is of relevance for policy making by addressing the relative sensitivity of impacts to key climate and socio-economic drivers, and the urgency for action expressed as a time series of the likelihood of crossing critical impact thresholds. It also examines options to respond by incorporating alternative adaptation actions in the analysis framework, which may be useful for exploring the types, choice and timing of adaptation responses.

Hourly future climate scenario datasets for impact assessment of climate change considering simultaneous interactions among multiple meteorological factors.

Assessing the impacts of climate change in multiple fields, such as energy, land and water resources, and human health and welfare is important to find effective strategies to adapt to a changing climate and to reduce greenhouse gases. Many phenomena influenced by climate change have diurnal fluctuations and are affected by simultaneous interactions among multiple meteorological factors. However, climate scenarios with detailed (at least hourly) resolutions are usually not available. To assess the impact of climate change on such phenomena while considering simultaneous interactions (e.g., synergies), climate scenarios with hourly fluctuations are indispensable. However, because meteorological indicators are not independent, the value of one indicator varies as a function of other indicators. Therefore, it is almost impossible to determine the functions that show all relationships among meteorological elements considering the geographical and temporal (both seasonal and time of a day) characteristics. Therefore, generating hourly scenarios that include possible combinations of meteorological indicators for each hourly observation unit is a challenging problem. In this study, we provide secondary future climate scenario datasets that have hourly fluctuations with reasonable combinations of meteorological indicator values that are likely to occur simultaneously, without losing the long-term climate change trend in the existing daily climate scenarios based on global climate models. Historical hourly weather datasets observed from 2017 to 2019 (the reference years) are used to retrieve short-term fluctuations. Bias-corrected daily future climate scenario datasets generated using four global climate models (GFDL CM3, HadGEM2-ES, MIROC5, and MRI-CGCM3) and two Representative Concentration Pathways (RCP8.5 and 2.6) are used to model long-term climate change. A total of 48 different types of hourly future scenario datasets for five meteorological indicators (temperature, solar radiation, humidity, rainfall, and wind speed) were acquired, targeting a projection period from 2020 to 2080, for 10 weather stations in Japan. The generated hourly climate scenario datasets can be used to project the quantitative impacts of climate change on targeted phenomena considering simultaneous interactions among multiple meteorological factors.

Long-term projections of economic growth in the 47 prefectures of Japan: An application of Japan shared socioeconomic pathways.

Assessing climate change impacts on local communities is an urgent task for national and subnational governments. The impact assessment requires socioeconomic scenarios, including a long-term outlook for demographic and economic indices. In Japan, the National Institute for Environmental Studies developed the Japan Shared Socioeconomic Pathways (JPNSSPs) and presented regional population scenarios corresponding to five different storylines. However, there exists no quantitative information about changes in local economies under the population scenarios. This study examines the economic activities in Japan's 47 prefectures using statistical models and calculates changes in the major economic indices (e.g., production, capital stock, and labor population) until 2100. The economic projection is based on ten socioeconomic scenarios generated from the JPNSSP population scenarios and original productivity scenarios. The economic projection results clearly show that Japan's population aging and decline have catastrophic impacts on national and subnational economies. Even in the most optimistic scenario, assuming a massive influx of immigrants and fast productivity growth, the GDP growth rate becomes negative in the 2090s. In the most pessimistic scenario, the GDP growth rate becomes negative in 2028 and continues to decline. As a result, Japan's GDP decreases to the level of the 1970s by 2100. The improvement of productivity cannot offset the GDP shrink caused by demographic changes. Furthermore, the population aging and decline accelerate the wealth concentration in urban areas. The Theil index, calculated using the economic projection results, shows increasing trends in all the scenarios. Tokyo's presence in Japan's economy will continue to increase throughout this century. Meanwhile, Kanagawa and Saitama, which belong to the top five prefectures in terms of economic production, may lose their positions. The Tohoku region, already suffering from population decline, will face severe economic stagnation. Our findings suggest that the depressing future is inevitable unless Japan overcomes the population aging and decline.

Heat health risk assessment in Philippine cities using remotely sensed data and social-ecological indicators.

More than half of the world's population currently live in urban areas and are particularly at risk from the combined effects of the urban heat island phenomenon and heat increases due to climate change. Here, by using remotely sensed surface temperature data and social-ecological indicators, focusing on the hot dry season, and applying the risk framework of the Intergovernmental Panel on Climate Change, we assessed the current heat health risk in 139 Philippine cities, which account for about 40% of the country's total population. The cities at high or very high risk are found in Metro Manila, where levels of heat hazard and exposure are high. The most vulnerable cities are, however, found mainly outside the national capital region, where sensitivity is higher and capacity to cope and adapt is lower. Cities with high levels of heat vulnerability and exposure must be prioritized for adaptation. Our results will contribute to risk profiling in the Philippines and to the understanding of city-level heat health risks in developing regions of the Asia-Pacific.

Integrated Climate-Change Assessment Scenarios and Carbon Dioxide Removal.

To halt climate change this century, we must reduce carbon dioxide (CO2) emissions from human activities to net zero. Any emission sources, such as in the energy or land-use sectors, must be balanced by natural or technological carbon sinks that facilitate CO2 removal (CDR) from the atmosphere. Projections of demand for large-scale CDR are based on an integrated scenario framework for emission scenarios composed of emission profiles as well as alternative socio-economic development trends and social values consistent with them. The framework, however, was developed years before systematic reviews of CDR entered the literature. This primer provides an overview of the purposes of scenarios in climate-change research and how they are used. It also introduces the integrated scenario framework and why it came about. CDR studies using the scenario framework, as well as its limitations, are discussed. Possible future developments for the scenario framework are highlighted, especially in relation to CDR.

Gridded emissions and land-use data for 2005-2100 under diverse socioeconomic and climate mitigation scenarios.

Information on global future gridded emissions and land-use scenarios is critical for many climate and global environmental modelling studies. Here, we generated such data using an integrated assessment model (IAM) and have made the data publicly available. Although the Coupled Model Inter-comparison Project Phase 6 (CMIP6) offers similar data, our dataset has two advantages. First, the data cover a full range and combinations of socioeconomic and climate mitigation levels, which are considered as a range of plausible futures in the climate research community. Second, we provide this dataset based on a single integrated assessment modelling framework that enables a focus on purely socioeconomic factors or climate mitigation levels, which is unavailable in CMIP6 data, since it incorporates the outcomes of each IAM scenario. We compared our data with existing gridded data to identify the characteristics of the dataset and found both agreements and disagreements. This dataset can contribute to global environmental modelling efforts, in particular for researchers who want to investigate socioeconomic and climate factors independently.

Downscaling Global Emissions and Its Implications Derived from Climate Model Experiments.

In climate change research, future scenarios of greenhouse gas and air pollutant emissions generated by integrated assessment models (IAMs) are used in climate models (CMs) and earth system models to analyze future interactions and feedback between human activities and climate. However, the spatial resolutions of IAMs and CMs differ. IAMs usually disaggregate the world into 10-30 aggregated regions, whereas CMs require a grid-based spatial resolution. Therefore, downscaling emissions data from IAMs into a finer scale is necessary to input the emissions into CMs. In this study, we examined whether differences in downscaling methods significantly affect climate variables such as temperature and precipitation. We tested two downscaling methods using the same regionally aggregated sulfur emissions scenario obtained from the Asian-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model. The downscaled emissions were fed into the Model for Interdisciplinary Research on Climate (MIROC). One of the methods assumed a strong convergence of national emissions intensity (e.g., emissions per gross domestic product), while the other was based on inertia (i.e., the base-year remained unchanged). The emissions intensities in the downscaled spatial emissions generated from the two methods markedly differed, whereas the emissions densities (emissions per area) were similar. We investigated whether the climate change projections of temperature and precipitation would significantly differ between the two methods by applying a field significance test, and found little evidence of a significant difference between the two methods. Moreover, there was no clear evidence of a difference between the climate simulations based on these two downscaling methods.

Global land-use allocation model linked to an integrated assessment model.

We developed a global land-use allocation model that can be linked to integrated assessment models (IAMs) with a coarser spatial resolution. Using the model, we performed a downscaling of the IAMs' regional aggregated land-use projections to obtain a spatial land-use distribution, which could subsequently be used by Earth system models for global environmental assessments of ecosystem services, food security, and climate policies. Here we describe the land-use allocation model, discuss the verification of the downscaling technique, and explain the influences of the downscaling on estimates of land-use carbon emissions. A comparison of the emissions estimated with and without downscaling suggested that the land-use downscaling would help capture the spatial distribution of carbon stock density and regional heterogeneity of carbon emissions caused by cropland and pasture land expansion.

Climate change impact and adaptation assessment on food consumption utilizing a new scenario framework.

We assessed the impacts of climate change and agricultural autonomous adaptation measures (changes in crop variety and planting dates) on food consumption and risk of hunger considering uncertainties in socioeconomic and climate conditions by using a new scenario framework. We combined a global computable general equilibrium model and a crop model (M-GAEZ), and estimated the impacts through 2050 based on future assumptions of socioeconomic and climate conditions. We used three Shared Socioeconomic Pathways as future population and gross domestic products, four Representative Concentration Pathways as a greenhouse gas emissions constraint, and eight General Circulation Models to estimate climate conditions. We found that (i) the adaptation measures are expected to significantly lower the risk of hunger resulting from climate change under various socioeconomic and climate conditions. (ii) population and economic development had a greater impact than climate conditions for risk of hunger at least throughout 2050, but climate change was projected to have notable impacts, even in the strong emission mitigation scenarios. (iii) The impact on hunger risk varied across regions because levels of calorie intake, climate change impacts and land scarcity varied by region.