• Energy Research

Most studies that have assessed impacts on mortality of future temperature increases have relied on a small number of simulations and have not addressed the variability and sources of uncertainty in their mortality projections.We assessed the variability of temperature projections and dependent future mortality distributions, using a large panel of temperature simulations based on different climate models and emission scenarios.We used historical data from 1990 through 2007 for Montreal, Quebec, Canada, and Poisson regression models to estimate relative risks (RR) for daily nonaccidental mortality in association with three different daily temperature metrics (mean, minimum, and maximum temperature) during June through August. To estimate future numbers of deaths attributable to ambient temperatures and the uncertainty of the estimates, we used 32 different simulations of daily temperatures for June-August 2020-2037 derived from three global climate models (GCMs) and a Canadian regional climate model with three sets of RRs (one based on the observed historical data, and two on bootstrap samples that generated the 95% CI of the attributable number (AN) of deaths). We then used analysis of covariance to evaluate the influence of the simulation, the projected year, and the sets of RRs used to derive the attributable numbers of deaths.We found that < 1% of the variability in the distributions of simulated temperature for June-August of 2020-2037 was explained by differences among the simulations. Estimated ANs for 2020-2037 ranged from 34 to 174 per summer (i.e., June-August). Most of the variability in mortality projections (38%) was related to the temperature-mortality RR used to estimate the ANs.The choice of the RR estimate for the association between temperature and mortality may be important to reduce uncertainty in mortality projections.

This study presents the results of an integrated model developed to evaluate the environmental and health impacts of Electric Vehicle (EV) deployment in a large metropolitan area. The model combines a high-resolution chemical transport model with an emission inventory established with detailed transportation and power plant information, as well as a framework to characterize and monetize the health impacts. Our study is set in the Greater Toronto and Hamilton Area (GTHA) in Canada with bounding scenarios for 25% and 100% EV penetration rates. Our results indicate that even with the worst-case assumptions for EV electricity supply (100% natural gas), vehicle electrification can deliver substantial health benefits in the GTHA, equivalent to reductions of about 50 and 260 premature deaths per year for 25% and 100% EV penetration, compared to the base case scenario. If EVs are charged with renewable energy sources only, then electrifying all passenger vehicles can prevent 330 premature deaths per year, which is equivalent to $3.8 Billion (2016$CAD) in social benefits. When the benefit of EV deployment is normalized per vehicle, it is higher than most incentives provided by the government, indicating that EV incentives can generate high social benefits.

AbstractMany studies have projected malaria risks with climate change scenarios by modelling one or two environmental variables and without the consideration of malaria control interventions. We aimed to predict the risk of malaria with climate change considering the influence of rainfall, humidity, temperatures, vegetation, and vector control interventions (indoor residual spraying (IRS) and long-lasting insecticidal nets (LLIN)). We used negative binomial models based on weekly malaria data from six facility-based surveillance sites in Uganda from 2010–2018, to estimate associations between malaria, environmental variables and interventions, accounting for the non-linearity of environmental variables. Associations were applied to future climate scenarios to predict malaria distribution using an ensemble of Regional Climate Models under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Predictions including interaction effects between environmental variables and interventions were also explored. The results showed upward trends in the annual malaria cases by 25% to 30% by 2050s in the absence of intervention but there was great variability in the predictions (historical vs RCP 4.5 medians [Min–Max]: 16,785 [9,902–74,382] vs 21,289 [11,796–70,606]). The combination of IRS and LLIN, IRS alone, and LLIN alone would contribute to reducing the malaria burden by 76%, 63% and 35% respectively. Similar conclusions were drawn from the predictions of the models with and without interactions between environmental factors and interventions, suggesting that the interactions have no added value for the predictions. The results highlight the need for maintaining vector control interventions for malaria prevention and control in the context of climate change given the potential public health and economic implications of increasing malaria in Uganda.

Objectives: We propose a novel approach to examine vulnerability in the relationship between heat and years of life lost and apply to neighborhood social disparities in Montreal and Paris. Methods: We used historical data from the summers of 1990 through 2007 for Montreal and from 2004 through 2009 for Paris to estimate daily years of life lost social disparities (DYLLD), summarizing social inequalities across groups. We used Generalized Linear Models to separately estimate relative risks (RR) for DYLLD in association with daily mean temperatures in both cities. We used 30 climate scenarios of daily mean temperature to estimate future temperature distributions (2021–2050). We performed random effect meta-analyses to assess the impact of climate change by climate scenario for each city and compared the impact of climate change for the two cities using a meta-regression analysis. Results: We show that an increase in ambient temperature leads to an increase in social disparities in daily years of life lost. The impact of climate change on DYLLD attributable to temperature was of 2.06 (95% CI: 1.90, 2.25) in Montreal and 1.77 (95% CI: 1.61, 1.94) in Paris. The city explained a difference of 0.31 (95% CI: 0.14, 0.49) on the impact of climate change. Conclusion: We propose a new analytical approach for estimating vulnerability in the relationship between heat and health. Our results suggest that in Paris and Montreal, health disparities related to heat impacts exist today and will increase in the future.

The potential impacts of climate change (CC) on Occupational Health and Safety (OHS) have been studied a little in tropical countries, while they received no attention in northern industrialized countries with a temperate climate. This work aimed to establish an overview of the potential links between CC and OHS in those countries and to determine research priorities for Quebec, Canada. A narrative review of the scientific literature (2005-2010) was presented to a working group of international and national experts and stakeholders during a workshop held in 2010. The working group was invited to identify knowledge gaps, and a modified Delphi method helped prioritize research avenues. This process highlighted five categories of hazards that are likely to impact OHS in northern industrialized countries: heat waves/increased temperatures, air pollutants, UV radiation, extreme weather events, vector-borne/zoonotic diseases. These hazards will affect working activities related to natural resources (i.e. agriculture, fishing and forestry) and may influence the socioeconomic context (built environment and green industries), thus indirectly modifying OHS. From this consensus approach, three categories of research were identified: 1) Knowledge acquisition on hazards, target populations and methods of adaptation; 2) Surveillance of diseases/accidents/occupational hazards; and 3) Development of new occupational adaptation strategies.