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Energy transitions in Indigenous, Northern and remote communities in Canada promise benefits that go beyond reliable, clean and affordable energy services. The Federal and Provincial governments have committed funding to get remote communities off diesel, acknowledging energy transitions' global and local benefits. Besides climate change mitigation, other benefits, including job creation, income generation, community ownership and local economic growth, are fundamental components of the value proposition of renewable energy projects. However, despite the promises, little evidence of the impacts of renewable energy on communities' local conditions exists. This article looks at the relationship between renewable energy projects and community well-being in Canada. We construct a panel of Indigenous communities and well-being levels using Census data and information about renewable energy projects for the period 1981–2016. The findings suggest that renewable energy is associated with higher levels of well-being. Concretely, having access to renewable energy increases overall well-being by 1 to 5 points on the 0–100 well-being scale, depending on the component of the well-being index considered in the analysis. • Renewable energy and community wellbeing is an essential relationship for successful energy transitions. • The size of the renewable energy projects is strongly associated with higher community wellbeing. • Communities that hold majority ownership of renewable energy projects exhibit higher wellbeing levels. • Solar and wind energy projects are associated with higher wellbeing levels, whereas hydropower is associated with lower ones. [ABSTRACT FROM AUTHOR]

• Innovate a BaPMI method for macro-scale distributed hydrologic modeling. • Apply it to a typical large cold-region watershed, Athabasca River Basin in Canada. • Verify feasibility & advantages in characterizing macro-scale cold-region hydrology. • Quantify parametric & hyperparametric sensitivities for facilitating its applications. • Reveal variabilities of climate-driven cross-scale uppermost hydro-variational effects. At macroscales (e.g., large river basins, nations, continents, or the globe), hydro-hazard mitigation under climate change requires accurate, applicable hydrologic models, especially over cold regions of complicated hydroclimatic relations. As an effort to address such a challenge, we innovate a macroscale distributed hydro-modeling method, Bayesian principal-monotonicity inference (BaPMI), based on climate classification, representative-grids selection, statistical hydrology, surrogates-based Bayesian optimization, and variance-based sensitivity analyses. The method is applied to a typical large cold-region watershed, i.e., Athabasca River Basin in Canada, and reveals a series of findings such as the following representatives. BaPMI shows promising skills in quantitatively characterizing macroscale cold-region hydrology under overall impacts of heterogeneous climates. Bayesian optimization of which hyperparameters are insensitive decreases hydro-modeling computational time by up to 96.3% and, together with climate classification, largely enhances BaPMI applicability while ensuring modeling accuracies. Individual BaPMI hydro-model parameters can explain over ¾ of the variation of hydro-modeling accuracies and, due to their interaction, joint calibration is required for avoiding underestimation of the impacts. Climatic conditions dominating cross-scale uppermost hydrologic variations consist of night temperature (40%), day temperature (38%), and precipitation (22%) for all catchments; all river flows along the mainstem are associated with upstream climatic events such as glacier melts. Climatic impacts decline from upstream to downstream and from summer, spring, winter to autumn; this may relate to tempo-spatial heterogeneity of soil, vegetation, geology, human interference and other non-climatic factors. Without this study, macroscale distributed hydrologic modeling would lack one advanced method (i.e., BaPMI) that can immunize against effects of data uncertainty, subjective judgement and climatic collinearity, adapt to complicated hydroclimatic relations and diverse hydro-variable(s) distributions, reveal cross-scale uppermost hydrologic variations and, through inexpensive computations, enhance hydro-modeling accuracies to avoid climatic-impact underestimation. The findings of this study could facilitate BaPMI applications and advance macroscale (cold-region) hydrology. [ABSTRACT FROM AUTHOR]

The deep decarbonization of the power systems is pivotal to the efforts of global energy transition and climate mitigation. The small modular reactor (SMR) is a decarbonized, reliable and secure generating technology that can bring new possibilities for global power-sector transition. However, such a transition involves a variety of complexities even beyond conventional systems. We comprehensively explore the SMR deployment and the decarbonization pathway in a number of diversified power and jurisdictional systems under various complexities associated with multiple variables, objectives, criteria and constraints that have dynamic and interactive relationships. This effort is based on the development of an SMR-embedded multi-region power system management model (SMPM). Using Canada as an example, here we show that SMRs would play different roles in diversified power systems, particularly for fossil-fuel dependent, highly variable renewable energy-penetrated and off-grid. The introduction of SMRs to Canada would result in up to 71% (/48%) emission reduction from 2005 (/2018) level in the country by 2045. Our results would help show the significance of SMRs for supporting the global energy transition. [Display omitted] • An SMPM model is developed to analyze low-carbon power system transition in Canada. • Multiple complexities and uncertainties in power systems are reflected. • SMR development issue is investigated within a power-system optimization framework. • The regional development of Canada is analyzed by a holistic optimization method. • GHG emissions from Canada's power sector would be 48% lower in 2045 than in 2018. [ABSTRACT FROM AUTHOR]

Natural climate solutions adopted by agricultural producers, such as cover crops, avoided grassland conversion, and improved nutrient management, can contribute to climate change mitigation while producing social and environmental co-benefits. Scaling natural climate solutions in agriculture is increasingly being explored as a critical component for achieving net-zero targets. There is growing recognition, however, that the hoped-for benefits of natural climate solutions will not be realized without a governance ecosystem that enables adoption. While many governance mechanisms have emerged to promote natural climate solutions in agriculture such as ecosystem service markets, climate innovation and clean tech funds, and producer education and outreach programs, little attention has been paid to how these governance mechanisms effectively contribute to an enabling governance ecosystem and where they fit within. In response, this paper compiles and synthesizes recommendations from relevant research fields including natural climate solutions, agri-environmental stewardship, and environmental governance to formulate a framework for natural climate solutions in agriculture. The framework manifests as a list of ten normative criteria organized by macro, sector, and geographical scales, which, if realized, can be expected to scale natural climate solutions adoption in agriculture. To demonstrate the utility of the framework, it is used to evaluate efforts to promote natural climate solutions in Canada's agriculture sector. This evaluation reveals an approach that is progressing towards meeting the criteria of the framework. However, in its current state, the governance ecosystem that influences natural climate solutions adoption in Canada's agriculture sector is project specific, with a patchwork of governance mechanisms largely operating parallel to one another, such that only some regional successes are possible. The framework is a starting point for identifying what might define an enabling governance ecosystem for natural climate solutions adoption in agriculture based on literature, highlighting the opportunity for future empirical research to build upon this framework towards a more complete view. • An enabling governance system should be developed to scale NCS adoption in agriculture. • A framework to evaluate and organize governance systems that influence NCS in agriculture is presented. • The application of the framework to Canada reveals an approach that is progressing towards meeting the criteria. • The current governance system in Canada reflects a patchwork of project specific mechanisms. • Further research is needed on the governance of NCS in agriculture. [ABSTRACT FROM AUTHOR]

The success and rate of forest regeneration has consequences for sustainable forest management, climate change mitigation, and biodiversity, among others. Systematically monitoring forest regeneration over large and often remote areas is challenging. Remotely sensed data and associated analytical approaches have demonstrated consistent and transparent options for spatially-explicit characterization of vegetation return following disturbance. Moreover, time series of satellite imagery enable the establishment of spatially meaningful recovery baselines that can provide a benchmark for identifying areas that are either under- or over-performing relative to those baselines. This information allows for the investigation and/or prioritization of areas requiring some form of management intervention, including guiding tree planting initiatives. In this research, we assess recovery following stand replacing disturbances for the 650 Mha forested ecosystems of Canada for the period 1985–2017, wherein ~51 Mha of Canada's forested ecosystems were impacted by wildfire, and ~ 21 Mha were impacted by harvesting. For quantification of forest recovery, we implement the Years to Recovery or Y2R metric using Landsat time series data based on the Normalized Burn Ratio (NBR) to relate the number of years required for a pixel to return to 80% of its pre-disturbance NBR value. By the end of the analyzed period, 76% of areas impacted by wildfire were considered spectrally recovered compared to 93% of harvested areas. On average, we found that harvest areas had more rapid spectral recovery (mean Y2R = 6.1 years) than wildfire (mean Y2R = 10.6 years) and importantly, that Y2R varied by ecozone, disturbance type, pre-disturbance land cover, and latitude. We used airborne laser scanning data to assess whether pixels that were considered spectrally recovered had attained United Nations Food and Agricultural Organization benchmarks of canopy height (>5 m) and cover (>10%) across four geographic regions representing different forest types. Overall, 87% and 97% of recovered pixels sampled in harvests and wildfires, respectively, had achieved at least one of the benchmarks, with benchmarks of height more readily achieved than benchmarks of cover. By analyzing spatial patterns of Y2R, we identified areas that had significant positive or negative spatial clustering in their rate of spectral recovery. Approximately 3.5–4% of areas disturbed by wildfire or harvest had significant positive spatial clustering, indicative of slower spectral recovery rates; these areas were also less likely to have attained benchmarks of height and cover. Conversely, we identified significant negative spatial clustering for 0.94% of areas recovering from harvest and 1.93% of areas recovering from wildfire, indicative of spectral recovery that was more rapid than the ecozonal baseline. Herein, we demonstrated that remote sensing can provide spatial intelligence on the nature of disturbance-recovery dynamics in forested ecosystems over large areas and moreover, can retrospectively quantify and characterize historic forest recovery trends within the past three decades that have implications for forest management, climate change mitigation, and restoration initiatives in the near term. • Landsat time series monitored forest disturbance and recovery for 32 years across 650 Mha. • Harvests spectrally recovered more rapidly (6.1 years) than wildfire (10.6 years). • Lidar-derived benchmarks of canopy cover (>10%) and height (>5 m) used for validation. • 87% and 97% of recovered harvest and wildfire samples achieved one of the benchmarks. • Nationally, 3.5%–4% of wildfire and harvest areas had slower than expected recovery. [ABSTRACT FROM AUTHOR]