Economic Impacts from PM_2.5 Pollution-Related Health Effects in China: A Provincial-Level Analysis

This study evaluates the PM_2.5 pollution-related health impacts on the national and provincial economy of China using a computable general equilibrium (CGE) model and the latest nonlinear exposure–response functions. Results show that the health and economic impacts may be substantial in provinces with a high PM_2.5 concentration. In the WoPol scenario without PM_2.5 pollution control policy, we estimate that China experiences a 2.00% GDP loss and 25.2 billion USD in health expenditure from PM_2.5 pollution in 2030. In contrast, with control policy in the WPol scenario, a control investment of 101.8 billion USD (0.79% of GDP) and a gain of 1.17% of China’s GDP from improving PM_2.5 pollution are projected. At the provincial level, GDP loss in 2030 in the WoPol scenario is high in Tianjin (3.08%), Shanghai (2.98%), Henan (2.32%), Beijing (2.75%), and Hebei (2.60%) and the top five provinces with the highest additional health expenditure are Henan, Sichuan, Shandong, Hebei, and Jiangsu. Controlling PM_2.5 pollution could bring positive benefits in two-thirds of provinces. Tianjin, Shanghai, Beijing, Henan, Jiangsu, and Hebei experience most benefits from PM_2.5 pollution control as a result of a higher PM_2.5 pollution and dense population distribution. Conversely, the control investment is higher than GDP gain in some underdeveloped provinces, such as Ningxia, Guizhou, Shanxi, Gansu, and Yunnan

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

Consequence of Climate Mitigation on the Risk of Hunger

Climate change and mitigation measures have three major impacts on food consumption and the risk of hunger: (1) changes in crop yields caused by climate change; (2) competition for land between food crops and energy crops driven by the use of bioenergy; and (3) costs associated with mitigation measures taken to meet an emissions reduction target that keeps the global average temperature increase to 2 °C. In this study, we combined a global computable general equilibrium model and a crop model (M-GAEZ), and we quantified the three impacts on risk of hunger through 2050 based on the uncertainty range associated with 12 climate models and one economic and demographic scenario. The strong mitigation measures aimed at attaining the 2 °C target reduce the negative effects of climate change on yields but have large negative impacts on the risk of hunger due to mitigation costs in the low-income countries. We also found that in a strongly carbon-constrained world, the change in food consumption resulting from mitigation measures depends more strongly on the change in incomes than the change in food prices

Socioeconomic factors and future challenges of the goal of limiting the increase in global average temperature to 1.5 °C

<p>The Paris Agreement has confirmed that the ultimate climate policy goal is to hold the increase in the global average temperature to well below 2 °C above pre-industrial levels and to pursue efforts to limit the increase to 1.5 °C. Moving the goal from 2 °C to 1.5 °C calls for much more concerted effort, and presents greater challenges and costs. This study uses an Asia-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) to evaluate the role of socioeconomic factors (e.g. technological cost and energy demand assumptions) in changing mitigation costs and achieving the 1.5 °C and 2 °C goals, and to identify the channels through which socioeconomic factors affect mitigation costs. Four families of socioeconomic factors were examined, namely low-carbon energy-supply technologies, end-use energy-efficiency improvements, lifestyle changes and biomass-technology promotion (technology cost reduction and social acceptance promotion). The results show that technological improvement in low-carbon energy-supply technologies is the most important factor in reducing mitigation costs. Moreover, under the constraints of the 1.5 °C goal, the relative effectiveness of other socioeconomic factors, such as energy efficiency improvement, lifestyle changes and biomass-related technology promotion, becomes more important in decreasing mitigation cost in the 1.5 °C scenarios than in the 2 °C scenarios.</p

Statistics for emissions density (kg SO₂/m²).

<p>SD, standard deviation; IQR, interquartile range. The regional codes are defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169733#pone.0169733.t001" target="_blank">Table 1</a>.</p

Regional classifications in the AIM/CGE.

<p>Regional classifications in the AIM/CGE.</p

Spatial distribution of sulfur emissions (Kg/m²/sec) in 2005 and 2100 in M1-CONV (sector total).

<p>Spatial distribution of sulfur emissions (Kg/m²/sec) in 2005 and 2100 in M1-CONV (sector total).</p

Emissions intensities (emissions per GDP) for countries in the Rest of Asia region based on M1-CONV and M2-INER.

<p>Emissions intensities (emissions per GDP) for countries in the Rest of Asia region based on M1-CONV and M2-INER.</p

Downscaling algorithm emission source groups and weight used.

<p>Downscaling algorithm emission source groups and weight used.</p