Climate change poses a serious threat to every sector of the economy. Agricultural sector is particularly vulnerable, due to its exposure to extreme conditions, temperature increases and systematic precipitation redistribution. How this susceptibility impacts crop production will have substantial repercussions on policies related to food security. Projecting the prospective impacts of climate change on crop production necessitates a comprehensive modelling system outlining crop responses to future conditions. Here we introduce a multivariate autoregressive econometrics model that includes a time-varying non-linear variable to account for the decreasing impact of technology on crop yields. Our model is designed to capture the relationships between technology, climate variables and the annual growth rate in crop yield across the world’s producing regions. Utilizing historical national crop production data and climate variables from 1961 to 2018, the developed model outperforms traditional panel regression methods. Additionally, a novel machine learning climate model emulator allows efficient estimation of crop production growth under a multitude of carbon equivalent emissions scenarios. Our key finding is that technological effects are prone to have a diminishing impact on wheat and rice production over time and may not adequately offset the negative effects of climate change under certain future emission scenarios. Naive assumptions surrounding technology result in overestimates of production exceeding 200% for wheat and 150% for rice. Our study implies that integrated assessment and other economic models that use oversimplified climate damage functions can compound inaccuracies in production estimates with adverse repercussions on policy decisions.

This paper introduces a Bayesian hierarchical modeling framework within a fully probabilistic setting for crop yield estimation, model selection, and uncertainty forecasting under multiple future greenhouse gas emission scenarios. By informing on regional agricultural impacts, this approach addresses broader risks to global food security. Extending an established multivariate econometric crop-yield model to incorporate country-specific error variances, the framework systematically relaxes restrictive homogeneity assumptions and enables transparent decomposition of predictive uncertainty into contributions from climate models, emission scenarios, and crop model parameters. In both in-sample and out-of-sample analyses focused on global wheat production, the results demonstrate significant improvements in calibration and probabilistic accuracy of yield projections. These advances provide policymakers and stakeholders with detailed, risk-sensitive information to support the development of more resilient and adaptive agricultural and climate strategies in response to escalating climate-related risks.

A positive Indian Ocean Dipole features an anomalously high west-minus-east sea surface temperature gradient along the equatorial Indian Ocean, affecting global extreme weathers. Whether the associated impact spills over to global economies is unknown. Here, we develop a nonlinear and country-heterogenous econometric model, and find that a typical positive event causes a global economic loss that increases for further two years after an initial shock, inducing a global loss of hundreds of billion US dollars, disproportionally greater to the developing and emerging economies. The loss from the 2019 positive event amounted to US$558B, or 0.67% in global economic growth. Benefit from a negative dipole event is far smaller. Under a high-emission scenario, a projected intensification in Dipole amplitude causes a median additional loss of US$5.6 T at a 3% discount rate, but likely as large as US$24.5 T. The additional loss decreases by 64% under the target of the Paris Agreement. The authors find a nonlinear, multiyear-long and country-heterogeneous economic loss induced by the Indian Ocean Dipole. Under a high emission scenario, the amplitude of the dipole is increasing, causing additional financial losses in the 21st century.

The El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) are the most prominent tropical climate variability affecting extreme weathers often with largescale socioeconomic impacts on global countries. Elusive are issues to what extent the impact affects the macroeconomy, how long the impact lasts, and how the impact may change in a warming climate. Using a smooth nonlinear climate-economy model fitted with historical data, here we find a damaging impact from El Niño and pIOD which increases for a further several years after initial shock. We attribute a loss of trillions and hundreds of billions US dollars globally for extreme El Niño and pIOD, which are far greater than previous estimates based on tangible losses. We find impacts from La Niña and nIOD are asymmetric and weaker. Under climate change, economic loss grows exponentially with increased ENSO and IOD variability. Exacerbated economic damage from changing ENSO and IOD should be considered in assessments of international mitigation strategies.

Navigating a path toward net-zero, requires the assessment of physical climate risks for a broad range of future economic scenarios, and their associated carbon concentration pathways. Climate models typically simulate a limited number of possible pathways, providing a small fraction of the data needed to quantify the physical risk. Here machine learning techniques are employed to rapidly and cheaply generate output mimicking these climate simulations. We refer to this approach as QuickClim, and use it here to reconstruct plausible climates for a multitude of concentration pathways. Higher mean temperatures are confirmed to coincide with higher end-of-century carbon concentrations. The climate variability uncertainty saturates earlier, in the mid-century, during the transition between current and future climates. For pathways converging to the same end-of-century concentration, the climate is sensitive to the choice of trajectory. In net-zero emission type pathways, this sensitivity is of comparable magnitude to the projected changes over the century.

This paper explores methods and the key factors influencing socio-economic analysis of the role of climate services in disaster risk reduction, with a regional emphasis on Small Island Developing States in the western tropical Pacific. We analyse the role of traditional benefit-cost analysis especially in the context of evaluating the importance of science-based climate change services (i.e., relevant to current and future climate change over multi-decadal timescales) in disaster risk reduction at a national economy level. Our analysis is premised on a range of relevant social and economic metrics at a national economy scale, including surrogate indicators for specific disaster risk reduction sensitive sectors in context of both mitigation (transitional risk) and adaptation (physical risk) to climate change. Relative importance of different methodologies of socio-economic analysis (i.e., partial/sectoral vs economy-wide modelling), gaps in relevant data and information, and the role of the public and private sectors in mobilising resources and capability for facilitating such analysis are explored. Our paper also discusses the issues relating to investing in, producing and undertaking on-ground applications associated with disaster risk reduction using climate change services for both public good and private (-for-profit) benefit outcomes, and provides suggestions for further research to improve socio-economic analysis of Climate Information Services impacts.

Patterns of natural resource use – including extraction, transformation, distribution and disposal of resources – are central to the dynamic links that connect human wellbeing and essential natural and social capital.

The global economy relies on a continuous and ever-growing throughput of biomass, fossil fuels, metal ores and non-metallic minerals to build, maintain and fuel the infrastructure of cities and industrial systems. The amount of materials required year by year depends on population levels, the use of technologies in production systems, investment into public infrastructure, and the lifestyle choices and consumption behaviours of households. The level of global material use has important consequences for environmental pressures and impacts of socioeconomic activities. Using materials more efficiently has positive implications for the economy and allows for reduced pressure on climate change, biodiversity loss, resource supply systems and waste management. The Shared Socioeconomic Pathways (SSPs) structure assumptions of future population growth, economic activity and urbanisation into five core narratives and allow for greater comparability of scenario assumptions and assessments of future natural resource requirements and pollution levels. In this study we explore the future of global material use across the five SSPs using economic modelling with industry sector, country detail and technology extensions and provide a summary of global material demand scenarios. The challenge faced to reduce global material flows is determined by the material requirements of different levels of population, global economic growth, the potential to improve material intensity of production systems, and consumer behaviour. Under the scenarios, global material flows vary between 134 billion tonnes for SSP1 and 282 billion tonnes for SSP5 by 2060. A middle of the road SSP2 scenario projects material demand of 176 billion tonnes by 2060, which is twice the level of 2017's global material use. Despite yearly average improvements in material efficiency of 0.8% between 2015 and 2060, our results demonstrate the level of ambition that is required to curb the increasing pressures on natural resource supply systems and their environmentally detrimental consequences. This study presents five alternative baseline scenarios which assume a continuation of historical trends in the way in which production, consumption and material use are interacting. Efforts to reduce global demand of primary materials need be stepped up to manage the global economy within planetary boundaries. This can be modelled by introducing ambitious policy settings which was, however, not the purpose of this study.

Climate change potentially affects the specialization of production and trade in agricultural markets. Previous studies suggest that changes in temperature and precipitation trend levels have a significant impact on yields, production, and commodity prices. This paper estimates the short-run and long-run effects of climate intra-annual anomalies variability, using temperature and precipitation, on agricultural trade across regions and economic levels. We employ ARDL-based mean-group type estimators on a panel data from 1962 to 2014 covering 102 countries. Our modelling controls for income, comparative advantage in land, productivity, and trade membership. We find a heterogeneous response to climate variability across our estimation panels. Precipitation affects total world agricultural exports, in particular livestock and dairy products; while temperature affects dairy and eggs exports. At the regional level, temperature has negative effects in the short-run in some regions, and a mixed influence in the long-run. Precipitation shows diverse regional impacts in the short and long-run. Further, we find that in the long-run, high-income countries are significantly affected by temperature and precipitation, while temperature positively affects developing countries. These findings on how agricultural export patterns are vulnerable to variations in climatic conditions could be used in further projections considering climate change as a determinant of agricultural trade.

There is a recognition that trade costs along domestic and international supply chains can be significantly reduced by improving the logistics performance in each mode of transport involved in various chain transactions. These improvements may be conveniently facilitated by the optimisation of the transitioning strategies from unimodal to multimodal (or combined) transport services. In this context, we examine here the status of current multimodal connectivity in freight transport practiced in the ASEAN region. We have focussed on the potential economic impacts of enhancing the levels of supply chain multimodal connectivity on the emerging economies of this region. Our methodology has two stages. First, we estimated a 'multimodal transport index' for ASEAN economies, using available data on performance indicators for maritime, air and land transport, combined with indicators of current logistics competence. Second, the Global Trade Analysis Project (GTAP) economy-wide model is used to estimate the economic impacts of ongoing transport connectivity enhancements in the ASEAN region, focussing on the emerging ASEAN members such as Cambodia, the Lao PDR, Myanmar and Viet Nam. It is important to note in this respect that the 10 ASEAN member economies are heterogeneous in nature in terms of economic growth and development. There are, for example, the more advanced economies such as Singapore and Malaysia, which contrast with the four emerging economies in the region. Our analysis of logistics performance in the ASEAN economies shows that (i) Singapore leads in performance indicators related to maritime transport, road density and logistics, (ii) Indonesia leads in primary airports and secondary airports, while (iii) Viet Nam has recorded the highest score in rail density. In addition, with respect to the performance indicators for maritime, primary airports, secondary airports and road density, there is a wide gap between the regional leader and most of the other ASEAN member economies, whilst our analysis of the logistics competence indicator shows moderately good performance in all of the ASEAN economies. Of particular interest to this investigation, is our analysis of the estimated multimodal transport index which indicates that, although Singapore appears to be leading in overall multimodal transport performance, Indonesia's economy has drawn almost level. Their performance is followed closely by Thailand, Viet Nam and Malaysia. The Philippines and Myanmar have modest, and similar levels of multimodal transport performance, whilst at the other end of the spectrum, the Lao PDR, Cambodia and Brunei have relatively low levels of multimodal transport performance. The economy-wide analysis shows that all else being equal, a 1.0 per cent improvement in factor productivity of the transport services in ASEAN will likely raise the real GDP in Cambodia, the Lao PDR, Myanmar and Viet Nam by 0.26, 0.15, 0.12 and 0.09 per cent respectively. Furthermore, it is estimated that a 1.0 per cent improvement in factor productivity of the transport services will increase consumer welfare in Cambodia, the Lao PDR, Myanmar and Viet Nam by US$ 28.9 million, US$ 11.7 million, US$ 73.5 million, and US$ 168.6 million respectively.

The increased levels of Greenhouse Gasses (GHGs) in the atmosphere will result in increased near-surface air temperature and absolute humidity. These two factors increasingly pose a risk of heat stress to humans. The Wet-Bulb Globe Temperature (WBGT) is a widely used and validated index for assessing the environmental heat stress. Using the output from the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations of the four Representative Concentration Pathways (RCPs), we calculated the global and regional changes in WBGT. Globally, the WBGT is projected to increase by 0.6–1.7 °C for RCP 2.6 and 2.37–4.4 °C for RCP 8.5. At the regional scale, our analysis suggests a disproportionate increase in the WBGT over northern India, China, northern Australia, Africa, Central America and Southeast Asia. An increase in WBGT has consequences not only on human health but also on social and economic factors. These consequences may be exacerbated in developing economies, which are less able to adapt to the changing environmental conditions.

In addition to expanding agricultural land area and intensifying crop yields, increasing the global trade of agricultural products is one mechanism that humanity has adopted to meet the nutritional demands of a growing population. However, climate change will affect the distribution of agricultural production and, therefore, food supply and global markets. Here we quantify the structural changes in the global agricultural trade network under the two contrasting greenhouse gas emissions scenarios by coupling seven Global Gridded Crop Models and five Earth System Models to a global dynamic economic model. Our results suggest that global trade patterns of agricultural commodities may be significantly different from today's reality with or without carbon mitigation. More specifically, the agricultural trade network becomes more centralised under the high CO2 emissions scenario, with a few regions dominating the markets. Under the carbon mitigation scenario, the trade network is more distributed and more regions are involved as either importers or exporters. Theoretically, the more distributed the structure of a network, the less vulnerable the system is to climatic or institutional shocks. Mitigating CO2 emissions has the co-benefit of creating a more stable agricultural trade system that may be better able to reduce food insecurity.

We study changes in crop cover under future climate and socio-economic projections. This study is not only organised around the global and regional adaptation or vulnerability to climate change but also includes the influence of projected changes in socio-economic, technological and biophysical drivers, especially regional gross domestic product. The climatic data are obtained from simulations of RCP4.5 and 8.5 by four global circulation models/earth system models from 2000 to 2100. We use Random Forest, an empirical statistical model, to project the future crop cover. Our results show that, at the global scale, increases and decreases in crop cover cancel each other out. Crop cover in the Northern Hemisphere is projected to be impacted more by future climate than the in Southern Hemisphere because of the disparity in the warming rate and precipitation patterns between the two Hemispheres. We found that crop cover in temperate regions is projected to decrease more than in tropical regions. We identified regions of concern and opportunities for climate change adaptation and investment.

Achieving sustainable development requires the decoupling of natural resource use and environmental pressures from economic growth and improvements in living standards. G7 leaders and others have called for improved resource efficiency, along with inclusive economic growth and deep cuts in global greenhouse emissions. However, the outlooks for and interactions between global natural resource use, resource efficiency, economic growth and greenhouse emissions are not well understood. We use a novel multi-regional modeling framework to develop projections to 2050 under existing trends and three policy scenarios. We find that resource efficiency could provide pro-growth pro-environment policies with global benefits of USD $2.4 trillion in 2050, and ease the politics of shifting towards sustainability. Under existing trends, resource extraction is projected to increase 119% from 2015 to 2050, from 84 to 184 billion tonnes per annum, while greenhouse gas emissions increase 41%, both driven by the value of global economic activity more than doubling. Resource efficiency and greenhouse abatement slow the growth of global resource extraction, so that in 2050 it is up to 28% lower than in existing trends. Resource efficiency reduces greenhouse gas emissions by 15-20% in 2050, with global emissions falling to 63% below 2015 levels when combined with a 2 degrees C emissions pathway. In contrast to greenhouse abatement, resource efficiency boosts near-term economic growth. These economic gains more than offset the near-term costs of shifting to a 2 degrees C emissions pathway, resulting in emissions in 2050 well below current levels, slower growth in resource extractions, and faster economic growth. Crown Copyright (C) 2017 Published by Elsevier Ltd. All rights reserved.

Pressure on developing economies to make quantifiable emissions reduction commitments has led to the introduction of intensity based emissions targets, where reductions in emissions are specified with reference to some measure of economic output. The Copenhagen commitments of China and India are two prominent examples. Intensity targets substantially increase the complexity of policy simulation and analysis, because a given emissions intensity target could be satisfied with a range of emissions and output combinations. Here, a simple algorithm, the Iterative Method, is proposed for an energy economic model to find a unique policy solution that achieves an emissions intensity target at minimum economic loss. We prove the mathematical properties of the algorithm, and compare its numerical performance with other methods’ in the existing literature.