Hans van Meijl

Climate change effects on agriculture: Economic responses to biophysical shocks

Significance Plausible estimates of climate change impacts on agriculture require integrated use of climate, crop, and economic models. We investigate the contribution of economic models to uncertainty in this impact chain. In the nine economic models included, the direction of management intensity, area, consumption, and international trade responses to harmonized crop yield shocks from climate change are similar. However, the magnitudes differ significantly. The differences depend on model structure, in particular the specification of endogenous yield effects, land use change, and propensity to trade. These results highlight where future research on modeling climate change impacts on agriculture should focus. Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change’s representative concentration pathway with end-of-century radiative forcing of 8.5 W/m2. The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

Climate change impacts on agriculture in 2050 under a range of plausible socioeconomic and emissions scenarios

Previous studies have combined climate, crop and economic models to examine the impact of climate change on agricultural production and food security, but results have varied widely due to differences in models, scenarios and input data. Recent work has examined (and narrowed) these differences through systematic model intercomparison using a high-emissions pathway to highlight the differences. This paper extends that analysis to explore a range of plausible socioeconomic scenarios and emission pathways. Results from multiple climate and economic models are combined to examine the global and regional impacts of climate change on agricultural yields, area, production, consumption, prices and trade for coarse grains, rice, wheat, oilseeds and sugar crops to 2050. We find that climate impacts on global average yields, area, production and consumption are similar across shared socioeconomic pathways (SSP 1, 2 and 3, as we implement them based on population, income and productivity drivers), except when changes in trade policies are included. Impacts on trade and prices are higher for SSP 3 than SSP 2, and higher for SSP 2 than for SSP 1. Climate impacts for all variables are similar across low to moderate emissions pathways (RCP 4.5 and RCP 6.0), but increase for a higher emissions pathway (RCP 8.5). It is important to note that these global averages may hide regional variations. Projected reductions in agricultural yields due to climate change by 2050 are larger for some crops than those estimated for the past half century, but smaller than projected increases to 2050 due to rising demand and intrinsic productivity growth. Results illustrate the sensitivity of climate change impacts to differences in socioeconomic and emissions pathways. Yield impacts increase at high emissions levels and vary with changes in population, income and technology, but are reduced in all cases by endogenous changes in prices and other variables.

Hotspots of uncertainty in land use and land cover change projections: a global scale model comparison

Abstract Model‐based global projections of future land‐use and land‐cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global‐scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.

Assessing uncertainties in land cover projections

Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.

Evaluating the macroeconomic impacts of bio-based applications in the EU

In 2012, the European Commission (EC) launched the Bioeconomy Strategy and Action Plan with the objective of establishing a resource efficient and competitive society that reconciles food security with the sustainable use of renewable resources. This report contributes to the plan by evaluating the macroeconomic impacts of bio-based applications in the EU. Such effects can only be evaluated with a computable general equilibrium model such as MAGNET. Four bio-based applications are considered, namely biofuel (second generation), biochemicals, bioelectricity, and biogas (synthetic natural gas). This is done assuming that 1 EJ lignocellulose biomass is converted into fuel, chemicals, electricity and gas and that the final product replaces an equal amount of conventional (e.g. fossil energy) product (on energy basis). The results show that given the assumed efficiency of conversion technology, costs of conversion, biomass price and oil price, the production of second generation biofuel and biochemicals are the only competitive sectors compare to their conventional counterparts in the year 2030 for the EU. In the case of the fuel sectors, it represents a net GDP effect of 5.1 billion US

Risk of increased food insecurity under stringent global climate change mitigation policy

while biochemicals generates 6 billion US

The impact of R&D on factor-augmenting technical change – an empirical assessment at the sector level

. A substantial part of this impact can be explained by the increase in wages, since the production of biomass is relatively labour intensive. The resulting increase in wages is transmitted to other sectors in the economy and increases production and consumption. Another important contributor is the lower oil and fuel price as a result of the substitution of oil based fuel production by bio-based fuel production, which in turn benefits the entire economy.

Assessing the Impact of Agricultural R&D Investments on Long-Term Projections of Food Security

Food insecurity can be directly exacerbated by climate change due to crop-production-related impacts of warmer and drier conditions that are expected in important agricultural regions1–3. However, efforts to mitigate climate change through comprehensive, economy-wide GHG emissions reductions may also negatively affect food security, due to indirect impacts on prices and supplies of key agricultural commodities4–6. Here we conduct a multiple model assessment on the combined effects of climate change and climate mitigation efforts on agricultural commodity prices, dietary energy availability and the population at risk of hunger. A robust finding is that by 2050, stringent climate mitigation policy, if implemented evenly across all sectors and regions, would have a greater negative impact on global hunger and food consumption than the direct impacts of climate change. The negative impacts would be most prevalent in vulnerable, low-income regions such as sub-Saharan Africa and South Asia, where food security problems are already acute.Economy-wide GHG emissions reductions may negatively affect food security. Stringent mitigation policies, modelled as carbon prices, are shown to lead to an increase in production costs, food prices and the population’s risk of hunger.

An Economic Assessment of the Potential and Costs of Investments in R&D in Agriculture to Avoid Land Use Change and Food Security Effects of Bioenergy

ABSTRACT The aim of the paper is to quantify endogenous factor-augmenting technical change driven by R&D investments in a panel of 11 OECD countries over 1987–2007. This paper contributes to the scant empirical evidence on the speed, sources and direction of technical change for various sectors and production factors. Assuming cost-minimization behavior, a CES framework is used to derive a system of equations that is estimated by a GMM system estimator. The estimated factor-augmenting technology parameters show that in most sectors, technical change was labor-augmenting and labor-saving. Statistically significant effects of manufacturing and services R&D were found on factor-augmenting technical change (with the highest R&D elasticities found in the high-tech manufacturing and transport, storage and communication sectors). Whereas ‘in-house’ R&D stimulates total factor productivity, R&D spilled over to other sectors has a capital-augmenting effect accompanied by a higher use of labor. The results of this study provide a starting point for incorporating endogenous factor-augmenting technical change in impact assessment models aimed at broad policy analysis including economic growth, food security or climate change.

A multi-scale, multi-model approach for analyzing the future dynamics of European land use

The purpose of this chapter is to analyze the impact of public agricultural Research and Development (R&D) investments on agricultural productivity and long-term food security to derive policy recommendations. The methodological approach is based on the application of the state-of-the art Computable General Equilibrium (CGE) model to R&D. By endogenizing R&D in global CGE models, it is possible to assess the impact of different public R&D policies on the food availability and food access of food security. This study found that R&D investments bring positive effects on the food access dimension of food security, particularly in places such as Sub-Saharan Africa where prices are expected to grow significantly by 2050, as agricultural land becomes scarcer and more expensive. Doubling the R&D intensity would soften the land constraints and substantially decelerate food prices, thus preventing the deterioration of living standards of rural households and leading to a gain in daily caloric consumption. The impact of alternative agricultural R&D policies on the various dimensions of food security has not been analyzed using a CGE framework, which enables capturing both the benefits and costs from R&D investments. Modeling the dynamic accumulation of R&D stocks makes it possible to analyze the effects of R&D on food security over time.