Ozone concentrations and damage for realistic future European climate and air quality scenarios

Ground level ozone poses a significant threat to human health from air pollution in the European Union. While anthropogenic emissions of precursor substances (NOx, NMVOC, CH4) are regulated by EU air quality legislation and will decrease further in the future, the emissions of biogenic NMVOC (mainly isoprene) may increase significantly in the coming decades if short-rotation coppice plantations are expanded strongly to meet the increased biofuel demand resulting from the EU decarbonisation targets. This study investigates the competing effects of anticipated trends in land use change, anthropogenic ozone precursor emissions and climate change on European ground level ozone concentrations and related health and environmental impacts until 2050. The work is based on a consistent set of energy consumption scenarios that underlie current EU climate and air quality policy proposals: a current legislation case, and an ambitious decarbonisation case. The Greenhouse Gas-Air Pollution Interactions and Synergies (GAINS) integrated assessment model was used to calculate air pollutant emissions for these scenarios, while land use change because of bioenergy demand was calculated by the Global Biosphere Model (GLOBIOM). These datasets were fed into the chemistry transport model LOTOS-EUROS to calculate the impact on ground level ozone concentrations. Health damage because of high ground level ozone concentrations is projected to decline significantly towards 2030 and 2050 under current climate conditions for both energy scenarios. Damage to plants is also expected to decrease but to a smaller extent. The projected change in anthropogenic ozone precursor emissions is found to have a larger impact on ozone damage than land use change. The increasing effect of a warming climate (+2–5 °C across Europe in summer) on ozone concentrations and associated health damage, however, might be higher than the reduction achieved by cutting back European ozone precursor emissions. Global action to reduce air pollutant emissions is needed to make sure that ozone damage in Europe decreases towards the middle of this century

The sensitivity of the costs of reducing emissions from deforestation and degradation (REDD) to future socioeconomic drivers and its implications for mitigation policy design

Climate change mitigation policies for the land use, land use change, and forestry (LULUCF) sector are commonly assessed based on marginal abatement cost curves (MACC) derived from optimization models or engineering approaches. Yet, little is known about the space of validity of MACCs and how they are influenced by changes in main underlying drivers. In this study, we apply the Global Forest Model (G4M) to explore the sensitivity of MACCs to variation of socioeconomic drivers of deforestation, afforestation, and forest management activities. Particularly, three key factors are considered: (I) wood price, as an indicator of timber market developments; (II) agricultural land price, as a proxy representing the developments on agricultural markets; and (III) corruption coefficient, representing the progress in institutional development and measuring abatement costs use efficiency. The results indicate that the MACCs are more sensitive to the corruption coefficient than to agricultural land price and wood price. Furthermore, we find that the MACCs are more robust with high carbon dioxide (CO2) price and that the sensitivity of the MACCs is higher at low CO2 prices. In general, it can be concluded that when assessing medium-term mitigation policies characterized by low CO2 prices, MACCs need to be developed in-line with institutions currently in place. When designing long-term mitigation policy characterized by high CO2 prices, the role of the analyzed drivers in MACCs estimation is less important

Spatially explicit assessment of roundwood and logging residues availability and costs for the EU28

Competition for woody biomass between material and energy uses is expected to further increase in the future, due to the limited availability of forest resources and increasing demand of wood for material and bioenergy. Currently, methodological approaches for modeling wood production and delivery costs from forest to industrial gates are missing. This study combines forest engineering, geographically explicit information, environmental constraints and economics in a bottom-up approach to assess cost–supply curves. The estimates are based on a multitude of wood supply systems that were assigned according to geographically explicit forestry characteristics. For each harvesting and transportation system, efficiencies were modeled according to harvesting sites and main delivery hubs. The cost–supply curves for roundwood and logging residues as estimates for current time and for the future (2030) show that there are large regional differences in the potential to increase extraction in the EU28. In most EU Member States, the costs of logging residues extraction increase exponentially already for low levels of mobilization, while extraction of roundwood can be increased to a larger extent within reasonable costs (30–40 $/m3). The large differences between countries in their harvest potential highlight the importance of spatially explicit analyses

Impacts of good practice policies on regional and global greenhouse gas emissions

The report looks at the impact of "good practice"emission reduction policies in nine different areas globally and across six countries: China, Brazil, India, the US, Russia and Japan. These include renewable energy, a variety of energy efficiency standards (buildings, car fuel efficiecy, appliances and lighting, industry), hydrofluorocarbons (HFC.s), emissions from fossil fuel production, electric cars and forestry. The authors looked at the most ambitious "good practice" policies around the world that are being implemented now, and calculated the difference these would make if everybody were to apply them. If all governments follow those governments that currently adopt the best climate policies in just nin different areas, they could reduce emissions close to the levels needed to stay on track to hold global warming below 2 degrees C. The implementation of good practice policies is projected to stabilise greenhouse gas emissions at 49-50 GtCO2e by 2020, and decrease to 44- 47 GtCO2e by 2030- close to the 2 degrees C emissions range (30-44 GtCO2e) by 2030. Direct replication of good practice policies is projected to halt emissions growth in most regions sinificantly before 2030. In contrast, current policies are expected to see emissions to increase to around 54 GtCO2e by 2020 and 59-60 GtCO2e by 2030

Global food efficiency of climate change mitigation in agriculture

Concerns exist regarding potential trade-offs between climate change mitigation in agriculture and food security. Against this background, the Global Biosphere Management Model (GLOBIOM) is applied to a range of scenarios of mitigation of emissions from agriculture to assess the implications of climate mitigation for agricultural production, prices and food availability. The " food efficiency of mitigation " (FEM) is introduced as a tool to make statements about how to attain desired levels of agricultural mitigation in the most efficient manner in terms of food security. It is applied to a range of policy scenarios which contrast a climate policy regime with full global collaboration to scenarios of fragmented climate policies that grant exemptions to selected developing country groups. Results indicate increasing marginal costs of abatement in terms of food calories and suggest that agricultural mitigation is most food efficient in a policy regime with global collaboration. Exemptions from this regime cause food efficiency losses

Sensitivity of marginal abatement cost curves to variation of G4M parameters

Because of the G4M model non-linearity marginal abatement cost curves (MACCs) are sensitive to variation of the model parameters, irrespective of the fact that the same parameter variations are applied in both zero-CO2 price and non-zero-CO2 price runs. Since integrated assessment models in general are complex computer models with non-linearity one may expect all MACCs constructed using such models are sensitive to variation of the model parameters. The MACCs constructed using G4M are much more sensitive to parameter variation at a certain range of CO2 prices, usually low CO2 prices. The MACCs for total biomass CO2 emissions constructed using G4M are most sensitive to variation of corruption coefficient (measuring efficiency of use of abatement costs) and, on the second place, to agriculture land price. Experts applying MACCs for policy analysis must be aware of uncertainty features of the MACCs as the uncertainty can influence the outcome of the analysis

Climate change impacts and mitigation in the developing world: An Integrated Assessment of the Agriculture and Forestry Sectors. Policy Research Working Paper No. WPS 7477

This paper conducts an integrated assessment of climate change impacts and climate mitigation on agricultural commodity markets and food availability in low- and middle-income countries. The analysis uses the partial equilibrium model GLOBIOM to generate scenarios to 2080. The findings show that climate change effects on the agricultural sector will increase progressively over the century. By 2030, the impact of climate change on food consumption is moderate but already twice as large in a world with high inequalities than in a more equal world. In the long run, impacts could be much stronger, with global average calorie losses of 6 percent by 2050 and 14 percent by 2080. A mitigation policy to stabilize climate below 2 degrees C uniformly applied to all regions as a carbon tax would also result in a 6 percent reduction in food availability by 2050 and 12 percent reduction by 2080 compared to the reference scenario. To avoid more severe impacts of climate change mitigation on development than climate change itself, revenue from carbon pricing policies will need to be redistributed appropriately. Overall, the projected effects of climate change and mitigation on agricultural markets raise important issues for food security in the long run, but remain more limited in the medium term horizon of 2030. Thus, there are opportunities for low- and middle- income countries to pursue immediate development needs and thus prepare for later periods when adaptation needs and mitigation efforts will become the greatest

Toward carbon neutrality before 2060: Trajectory and technical mitigation potential of non-CO2 greenhouse gas emissions from Chinese agriculture

In 2020, China announced that it aims to achieve carbon neutrality before 2060. Despite the recognition of agriculture's importance in emission mitigation strategies, assessing the non-CO2 greenhouse gas (GHG) mitigation potentials from this sector remains technically and conceptually challenging. This study developed a bottom-up inventory-based model (the Agriculture-induced non-CO2 GreenHouse Gases INVentory model) to provide region-specific long-term projections (to 2060) of non-CO2 GHG emissions (including methane and nitrous oxide) from the Chinese agricultural sector. Seventeen production-side technologies were identified that could reduce on-farm emissions, and their mitigation potentials by 2060 were evaluated. Results showed that agricultural non-CO2 GHG emissions rose by 34% from 1980 to 2018, and they are projected to increase further by 33% to reach 1153 MtCO2-eq yr−1 by 2060. Implementing selected technological adaptations could lead to peak agricultural emissions before 2030 and then reduce them by 32%–50% by 2060. The most effective mitigation measures include feed supplements, feed quality improvements, slow-release fertilizers, and improved water management for paddy fields and uplands. All six regions of China will see a gradual increase in agricultural emissions. South Central China and Southwest China have the largest shares of total national emissions and the greatest mitigation potentials. However, technology adoption faces a series of socio-economic obstacles such as the high cost of technology promotion, smaller farm sizes, farmers' aversion to risk, and a complex set of objectives for agriculture