Carbon Emission Scenarios Research Articles

Changing ocean seasonal cycle escalates destructive marine heatwaves in a warming climate

Marine heatwaves (MHWs) can cause various adverse effects on marine ecosystems associated with complicated social ramifications. It has been well established that the gradually rising sea surface temperature (SST) due to anthropogenic carbon emission will cause an increase of the MHW duration and intensity. However, for species with strong adaptation capacity or mobility, MHW changes due to the altered SST variability under greenhouse warming are more crucial but so far remain poorly assessed. Under the high carbon emission scenario, we show that the cumulative duration (intensity) of MHWs, with the effect of secular SST increase excluded, is projected to be 60% (100%) higher by the end of this century than in the 1990s due to an amplified SST seasonal cycle. This increase becomes more evident for stronger MHWs, reaching up to 8 (30) folds for the extreme MHW category. The amplified SST seasonal cycle also causes pronounced seasonality of MHWs, making them more active in summer-autumn than winter-spring. Our results suggest that MHWs are likely to have increasingly devastating impacts on a wide range of marine species in the future without taking effective steps for carbon emission reduction.

Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning

Ocean acidification is a consequence of the absorption of anthropogenic carbon emissions and it profoundly impacts marine life. Arctic regions are particularly vulnerable to rapid pH changes due to low ocean buffering capacities and high stratification. Here, an unsupervised machine learning methodology is applied to simulations of surface Arctic acidification from two state-of-the-art coupled climate models. We identify four sub-regions whose boundaries are influenced by present-day and projected sea ice patterns. The regional boundaries are consistent between the models and across lower (SSP2-4.5) and higher (SSP5-8.5) carbon emissions scenarios. Stronger trends toward corrosive surface waters in the central Arctic Ocean are driven by early summer warming in regions of annual ice cover and late summer freshening in regions of perennial ice cover. Sea surface salinity and total alkalinity reductions dominate the Arctic pH changes, highlighting the importance of objective sub-regional identification and subsequent analysis of surface water mass properties.

Prediction of China's copper material flows under carbon emissions projections for the shared socioeconomic pathways

The production and utilization of metal resources are a major source of carbon emissions and will be constrained by future carbon peaking and carbon neutrality. Based on carbon emissions scenarios of Shared Socioeconomic Pathways (SSP1–5), we performed a life cycle assessment and material flow analysis to analyze future domestic copper production and utilization under the constraint of CO2 budgets in China. Our findings indicates that secondary copper has potential to substitute primary copper as a major component of copper production in the future. Copper in-use stock shows a peaking trend, while the flow intensity is projected to show an overall decreasing trend, indicating the beginning of a transitional period relating to economic growth and copper utilization. Meanwhile, the increase of recovery level shows contributions to copper production. Conclusions above suggest the importance of active resource recovery and the decoupling of metal production from economic activities.

Integrating IPAT and CLUMondo Models to Assess the Impact of Carbon Peak on Land Use

China’s growth plans include a carbon emission peak policy, which is a restriction that indirectly impacts land use structure. In this study, we simulate different paths for achieving policy objectives, and explore the linkages between those paths and land use change. The IPAT model was used to simulate the carbon emissions generated from a natural development scenario, an ideal policy scenario, and a retributive carbon emission scenario in China from 2020 to 2030. The simulation results were incorporated into the CLUMondo model as a demand driver to simulate the land use change in 2030. The results show that carbon emission peak policy can somewhat reduce carbon emissions and increase building land in a regulated way. However, the policy may also lead to a short-term surge in carbon emissions, a reactive expansion of arable land and building land. This may reduce losses in economic development when carbon emissions are limited, but does not achieve the integration of social, economic, and ecological goals. This study links the carbon emission peak policy with land use change and provides a fresh perspective on the Chinese government’s carbon reduction policy.

The impact of climate change on three indicator Galliformes species in the northern highlands of Pakistan.

The rise in global temperature is one of the main threats of extinction to many vulnerable species by the twenty-first century. The negative impacts of climate change on the northern highlands of Pakistan (NHP) could change the species composition. Range shifts and range reduction in the forested landscapes will dramatically affect the distribution of forest-dwelling species, including the Galliformes (ground birds). Three Galliformes (e.g., Lophophorus impejanus, Pucrasia macrolopha, and Tragopan melanocephalus) are indicator species of the environment and currently distributed in NHP. For this study, we used Maximum Entropy Model (MaxEnt) to simulate the current (average for 1960-1990) and future (in 2050 and 2070) distributions of the species using three General Circulation Models (GCMs) and two climate change scenarios, i.e., RCP4.5 (moderate carbon emission scenario) and RCP8.5 (peak carbon emission scenario). Our results indicated that (i) under all three climate scenarios, species distribution was predicted to both reduce and shift towards higher altitudes. (ii) Across the provinces in the NHP, the species were predicted to average lose around one-third (35%) in 2050 and one-half (47%) by 2070 of the current suitable habitat. (iii) The maximum area of climate refugia was projected between the altitudinal range of 2000 to 4000m and predicted to shift towards higher altitudes primarily > 3000m in the future. Our results help inform management plans and conservation strategies for mitigating the impacts of climate change on three indicator Galliforms species in the NHP.

UBEM.io: A web-based framework to rapidly generate urban building energy models for carbon reduction technology pathways

Policymakers are struggling to understand what specific mixes of building retrofitting upgrades are necessary to achieve carbon emission targets. Urban building energy modeling (UBEM) is a bottom-up simulation method to develop policy measures for building stocks. However, the use of UBEM tools requires hard-to-find individuals with training in multiple domains and significant setup time exacerbated through a lack of standardized building use and construction databases. To address these challenges, this paper presents UBEM.io, a novel web-based framework to rapidly generate UBEMs in an automated and scalable fashion with clear handover points. UBEM.io is designed to help any city or municipality conduct low-cost, rapid energy and carbon emissions scenario studies. UBEM.io contributes to practice and research in multiple ways: automated generation of urban-scale building geometries based on widely available inputs; assignment of building simulation templates from a pre-populated library; matching of templates to individual buildings via archetypes; and visualization of simulation results for various carbon emissions reduction pathways. The framework was piloted in Evanston, IL (USA) to build an UBEM comprising 1,363 buildings. It was then successfully deployed with representatives from eight municipalities: Braga (Portugal), Cairo (Egypt), Dublin (Ireland), Florianopolis (Brazil), Kiel (Germany), Middlebury, VT (USA), Montreal (Canada), and Singapore.

Carbon emissions dynamic simulation and its peak of cities in the Pearl River Delta Urban Agglomeration

<p indent=0mm>Cities account for more than 70% of global carbon emissions and play an important role in mitigating climate change and achieving carbon peak and carbon neutrality. As the Paris Agreement emphasizes the need to reach global peaking of greenhouse gas emissions as soon as possible, it is significant to predict carbon emissions at the city level. However, the current COVID-19 pandemic has dramatically impacted global socioeconomic development and carbon emissions, downplaying the reference value for most urban carbon emission prediction models. In fact, existing studies on urban carbon emission prediction have also suffered from some shortcomings, such as unclear analyses of the impact of the pandemic, single scenario prediction, unified setting of growth rates, and failure to provide decision support for the government’s carbon peak work. Therefore, a multi-scenario study on urban carbon emission prediction and carbon peak in the post-pandemic period would provide local governments with scientific data to make their carbon peak action plan. To that end, we set five-carbon emission scenarios: bussiness as usual (BAU), high emissions (HE), extremely high emissions (EHE), low emissions (LE) and extremely low emissions (ELE). Based on the Monte Carlo method, we adjust the probabilities of different periods and different carbon emission scenarios to simulate uncertain evolution of carbon emissions as well as carbon emission reduction. Combining with multi-scenario analyses with the Mann-Kendall trend test and Theil Sen’s trend slope estimation method, we predict carbon emissions of the Pearl River Delta Urban Agglomeration (PRD) from 2021 to 2035 and analyze the evolution path of PRD’s carbon emissions as well as its potential for carbon peak and carbon emission reduction from 2006 to 2035. Discussions are made on the possibility of achieving conditional areas’ carbon peak goal in 2025 in Guangdong and China’s carbon peak goal in 2030. We find that: (1) Carbon emissions of PRD increased rapidly from 2006 to 2016. Dynamic simulation shows that carbon emissions a significant peak in 2020 and decrease to 248.85 M~270.06 Mt in 2035. Carbon intensity decreases by 84.18%–85.21% from 2006 to 2035. Based on the emission reduction of the BAU scenario, the cumulative carbon emission reduction potential of the LE scenario and ELE scenario is as high as 304.86 M and 587.22 Mt from 2021 to 2035. Carbon emission reduction potential based on dynamic simulation of random combination scenario is between −81.68 and 128.25 Mt, with a probability of 67.65% to achieve further emission reduction. The probability of reducing 27.44 Mt carbon emissions is the largest. (2) Shenzhen, Zhuhai, Huizhou and Dongguan are four cities that show an inverted “U” shaped evolution path to achieve carbon peak. All of them reach the carbon peak no later than 2020. From 2006 to 2035, especially after the carbon peak, carbon emissions of these cities will decrease significantly. Their carbon emissions will reduce by 14.15 M–15.40 Mt, 9.17 M–9.94 Mt, 24.07 M–26.08 Mt and 22.36 M–24.24 Mt in 2035, respectively. The cumulative carbon emission reduction potential from 2021 to 2035 is −7.99 M–8.69 Mt, −3.48 M–4.87 Mt, −5.97 M–15.39 Mt and −8.77 M–12.62 Mt, respectively. However, being earlier to reach a carbon peak reduces their carbon emission reduction potential from 2021 to 2035. (3) Guangzhou, Foshan, Zhongshan, Jiangmen and Zhaoqing are five cities that could potentially reach carbon peaks but with divergent evolution paths. Some scenarios are at risk of not reaching a carbon peak. The possibility for Guangzhou, Foshan and Zhongshan to achieve the carbon peak target of conditional areas in Guangdong Province in 2025 is more than 96.01%, while that for Jiangmen and Zhaoqing is less than 20.08%. Moreover, there is a possibility of 2.04% for Jiangmen and Zhaoqing not to reach a carbon peak. In 2035, the emission reduction of the five cities will be 56.90 M–61.87 Mt, 44.35 M–48.16 Mt, 23.92 M–25.91 Mt, 33.78 M–36.58 Mt and 20.15 M–21.88 Mt, respectively. The cumulative carbon emission reduction potential of these cities from 2021 to 2035 is significant, which is −23.75M–26.60 Mt, −17.51 M–<sc>22.17 Mt,</sc> −6.64 M–12.19 Mt, −7.57 M–17.82 Mt and −3.86 M–11.79 Mt, respectively. (4) Being earlier to reach a carbon peak is conducive for cities to reduce carbon emissions. The curve of cumulative carbon emission reduction potential shows that the marginal potential of carbon emission reduction increases with time. So early adoption of emission reduction measures and early realization of carbon peak will promote carbon emission reduction. When making action plans for carbon peak, we should prevent cities from reaching false carbon peak during the platform period, pay attention to the demonstration and acceleration effect of carbon peak cities with relatively high carbon emissions, and explore the carbon emission reduction potential of cities that have difficulties in reaching carbon peak by optimizing their energy structure and utilization efficiency.

Investigating climate tipping points under various emission reduction and carbon capture scenarios with a stochastic climate model

We study the mitigation of climate tipping point transitions using an energy balance model. The evolution of the global mean surface temperature is coupled with the CO 2 concentration through the green-house effect. We model the CO 2 concentration with a stochastic delay differential equation (SDDE), accounting for various carbon emission and capture scenarios. The resulting coupled system of SDDEs exhibits a tipping point phenomena: if CO 2 concentration exceeds a critical threshold (around 478 ppm ), the temperature experiences an abrupt increase of about six degrees Celsius. We show that the CO 2 concentration exhibits a transient growth which may cause a climate tipping point, even if the concentration decays asymptotically. We derive a rigorous upper bound for the CO 2 evolution which quantifies its transient and asymptotic growths, and provides sufficient conditions for evading the climate tipping point. Combining this upper bound with Monte Carlo simulations of the stochastic climate model, we investigate the emission reduction and carbon capture scenarios that would avert the tipping point.

The fate of the City of Mutare's urban water supply as changing climate impacts on its source in the Pungwe sub‐catchment

AbstractSustaining a steady supply of water to urban communities is of importance in a period that is characterised by rapid urban population growth, a global pandemic, and a changing climate that threatens the availability of the resource from its sources. Water supply to the City of Mutare is from three sources, Small Bridge Dam, Odzani, and the Pungwe River. The Pungwe source provides better quality water resources equivalent to the combined quantity supplied by the other two. It becomes an important source for the city, but climate change threatens the availability of water resources in the southern African region. Thus, it is imperative to quantitatively assess the impacts of a changing climate on water resources to enable the development of sustainable management alternatives. Using two carbon emission scenarios Representative Concentration Pathways (RCPs) 4.5 and 8.5, the study assesses the future availability of water resources from the Pungwe River to the City of Mutare using the Pitman hydrological model applied in an uncertainty framework. Available historical streamflow observations at gauging station F14 indicate a Q95 flow of about 2 Mm3/year. Projected future water resources at the end of the 21st century show a slight increase of up to 2.38% under the low carbon emission scenario (RCP4.5) and a decrease of up to 9.73%under the high carbon emission scenario (RCP8.5). These model‐generated results are useful to water managers to plan for catchment management strategies that would ensure continuous urban water supply, and the identification and development of possible future alternative water sources.

Carbon emission scenario of conventional buildings

Carbon emission scenario of conventional buildings

Study of mesoscale NDVI prediction models in arid and semiarid regions of China under changing environments

Currently, studies analysing the relationship between climatic factors and vegetation may present some problems, including considering insufficient spatiotemporal characteristics, contradictory results and deficiencies in reflecting vegetation changes in the future. To address this, we first analysed the spatiotemporal characteristics of the impact of climatic factors on the normalized difference vegetation index (NDVI) using a multiple regression model constructed from the perspective of water deficit. Second, the two carbon emission scenarios provided by Coupled Model Inter-comparison Project phase 5 (CMIP5) were combined to predict the future vegetation conditions in China. Finally, trend analysis, Sen's slope and cycle analysis methods were employed to explore the trend of the NDVI and its spatiotemporal characteristics in the arid and semiarid regions of China. The results show that the impact of climate change on vegetation possesses distinct spatiotemporal characteristics, and the influences are different depending on the month and location. Vegetation has greened in most arid and semiarid areas of China during historical periods. In the future, NDVI variation will show a fluctuating tendency, and increasing and declining trends for the NDVI will alternately occur monthly. Although both precipitation and temperature will increase in the future, spring vegetation will be threatened by stress in some areas due to severe drought caused by the increase in evaporation, which is caused by increasing temperature. The impact of climatic factors on the amplitude and cycle of vegetation type zones varies monthly. The impact of carbon concentration on the NDVI in vegetation type zones was not significant but was relatively large in some areas. The influence of the carbon concentration on the NDVI cycle is different each month. Carbon concentration basically has no effect on amplitude.

A hierarchical decomposition approach for multi-level building design optimization

With the advancement in and integration of building energy simulation and optimization tools, building design optimization considering different design objectives and involving multiple disciplines has become technically feasible. However, this multifaceted approach unavoidably increases the dimension of the problem and comes with a prohibitively high computational cost. Hierarchical decomposition divides an optimization problem into several interconnected subproblems, and each subproblem has its objectives and constraints. Therefore, a complex problem is represented by a series of manageable subproblems. However, solving subproblems may lead to the local optimal solutions of the original problem. This research proposes a hierarchical decomposition approach that can achieve global optimal solutions. The proposed approach is applied to a multi-objective optimization problem to minimize the carbon emission and operating cost of building. This problem is decomposed into two subproblems to be solved sequentially. The first subproblem is to minimize the energy consumptions of the building. The second subproblem has the same objectives as the original one, but the search space is the result of the first subproblem. The benefit of decomposing this problem is that only the second problem needs to be solved again when additional energy price and carbon emission scenarios to be considered. A mid-rise residential building is selected as a case study to demonstrate the approach. The results show that the decomposition approach can reduce the number of simulations while keeping the full set of solutions of the original problem. The application of the proposed approach is based on a fixed utility rate, and only two objectives are considered for each level of optimization. These limitations could be overcome by adjusting the way to apply this approach.

Forecasting habitat and connectivity for pronghorn across the Great Basin ecoregion

AbstractAimsIn the sagebrush ecosystems of the western United States, identifying and enhancing habitat for large ungulates has become an increased priority for many management agencies, as indicated by Department of Interior Secretarial Order 3362. Estimating and understanding current and future habitat suitability and connectivity is important for successful long‐term management of these species.LocationGreat Basin ecoregion, western USA.MethodsWe focussed on pronghorn (Antilocapra americana) in the Great Basin ecoregion and used a variety of data sources (GPS telemetry, aerial surveys, observation locations) to develop multi‐scale ensemble habitat suitability models for the current and two future time steps (years 2050 and 2070). We also developed dynamic resistant kernels to model pronghorn connectivity. We combined the habitat suitability and connectivity outputs to derive and quantify changes in pronghorn habitat networks through time as well as identify areas that are resilient to climate and land use change.ResultsWe observed a 33.4% decline in highly suitable pronghorn habitat by 2070, assuming a high carbon emission scenario. Patches of suitable habitat reduced in number and size, whilst the distance amongst patches increased, indicating elevated importance of connectivity for pronghorn in the future. Future connectivity decreased to a greater degree than habitat suitability (47.2%–80.0%, depending on the pronghorn movement threshold used). We also found highly suitable habitat (70%) to be more resilient to climate change than areas of connectivity (10%–15%).Main conclusionsOur results show a loss of high‐quality pronghorn habitat and areas of connectivity with projected climate change. Connectivity was more sensitive than habitat, indicating connectivity may become a limiting factor for pronghorn populations in the Great Basin. These results can help managers prioritize resource investments and conservation efforts in areas most likely to be successful towards long‐term pronghorn conservation.

Carbon emission forecasting and scenario analysis in Guangdong Province based on optimized Fast Learning Network

As the most economically developed province in China, Guangdong is facing the severe challenge of reducing carbon emissions. The aim of this study is to explore whether Guangdong can achieve the carbon emission peak by 2030 and the carbon neutrality by 2060. We calculate the energy-related carbon emissions, technology carbon emissions from cement production and forest carbon sinks from 1995 to 2019, and construct a Fast Learning Network (FLN) forecasting algorithm improved by Chicken Swarm Optimization (CSO) to predict carbon emissions in 2020–2060. The superiority of CSO-FLN model is confirmed by three error indicators (MAE, MAPE, RMSE). Based on the different change rates of carbon emission influencing factors, nine scenarios are set up to pursue low-carbon development paths. The results show that (1) Guangdong's carbon emissions generally showed an upward trend in 1995–2019; (2) The forecasting effects of CSO-FLN model surpasses Fast Learning Network (FLN) and Extreme Learning Machine (ELM) by comparing the three error indicators; (3) For Guangdong Province, the peak of carbon emissions can be achieved by 2030 or before only under scenario 3, scenario 4, scenario 5 and scenario 7, while only on scenario 3, can carbon neutrality be realized by 2060. According to the research results, some countermeasures and suggestions to reduce carbon emissions are put forward.

Time of emergence of economic impacts of climate change

We introduce the concept of time of emergence of economic impacts (ToEI), which identifies the initial moment when the climate change impact signal exceeds a previously defined threshold of past economic output shocks in a given geographic area. We compute the ToEI using probabilistic climate change projections and impact functions from three integrated assessment models of climate change: DICE, RICE and CLIMRISK. Our results demonstrate that, in terms of the business-as-usual carbon emissions scenario, the global economy could reach its ToEI by 2095. Regional results highlight areas that are likely to reach the ToEI sooner, namely Western Europe by 2075, India by 2083, and Africa by 2085. We also explore local-scale variations in the ToEI demonstrating that, for example, Paris already reached the ToEI around 2020, while Shanghai will reach it around 2080. We conclude that the ToEI methodology can be applied to impact models of varying scales when sufficient historical impact data are available. Moreover, unprecedented impacts of climate change in the 21st century may be experienced even in economically developed regions in the US and Europe. Finally, moderate to stringent climate change mitigation policies could delay the extreme economic impacts of climate change by three decades in Latin America, the Middle East, and Japan, by two decades in India, Western Europe, and the US, and by one decade in Africa. Our results can be used by policymakers interested in implementing timely climate policies to prevent potentially large economic shocks due to climate change.

Impact of climate change on Asiatic black bear (Ursus thibetanus) and its autumn diet in the northern highlands of Pakistan.

Approximately 20%-30% of plant and animal species are at risk of extinction by the end of the 21st century owing to climate change. Range shifts and range contractions in plant species will dramatically affect the distribution of animals relying on them for food and shelter. The negative impacts of climate change on forested landscapes of the northern highlands of Pakistan (NHP) could change the species composition and distribution. The Asiatic black bear (Ursus thibetanus), a forest-dwelling species, primarily depends on plants for foraging, and is assumed to be affected by climate change in NHP. Scat analyses and indigenous knowledge from Machiara National Park revealed the maximum consumption of Quercus species (natural food) and Zea mays (human grown food) by the Asiatic black bear in autumn season. We collected the occurrence data of the Asiatic black bear and its commonly used food (three Quercus spp.) in the NHP. We used the MaxEnt model to simulate current and future (in 2050 and 2070) distribution of the species under RCP4.5 (medium carbon emission scenario) and RCP8.5 (extreme carbon emission scenario). The results predict range reduction and extreme fragmentation in the habitats of all the Quercus spp. Besides, a dramatic decrease in the suitable (SH) and very highly suitable (HSH) habitats was predicted in the future. Range shift and range reduction of Quercus spp. may interrupt the denning chronology of Asiatic black bears, escalate the human-black bear conflicts and local extirpation of the species. Given the extent and magnitude of climate change, it will likely not be enough to focus solely on the conservation of the Asiatic black bear. We need more dynamic planning aiming at mitigating the effect of climate change in forested landscapes including the Quercus forests.

Sustainable energy planning of residential sector: A case study of Bhanu municipality

This study analyzes the energy situation of Bhanu Municipality, Tanahun, Nepal and deals for future energy projection and sustainable energy planning. Municipality occupies an area of 184 sq. km. This research is mainly based on primary data and supported by secondary data from various sources. For collection of primary data, 152 random household samples were surveyed and the locals were interviewed on their annual consumption. Data analysis was done with the help of excel and LEAP software. Major energy sources in the Municipality are forests, water resource, agricultural residues, animal dung and solar. Analysis shows that the total energy consumption of Bhanu municipality is 635.67TJ with per capita consumption is 12.69 GJ. The main fuel for cooking in the residential sector is firewood with share of 80% due to easily accessible from private, government and community forests which covers 38% of the municipality area. Lighting is done through grid electricity, almost 98% of the households has access to Grid. The analysis was made through different scenarios in LEAP i.e., Low Carbon Emission Scenario, Efficient Cooking Scenario, Efficient Lighting Scenario based on Municipal plans and Sustainable Goal 7.Low emission scenario is the policy intervention scenario in which electrification of end use demands is done for limiting the GHGs emissions.

Plant developmental stage influences responses of Pinus strobiformis seedlings to experimental warming.

Seedling emergence, survival, morphological and physiological traits, and oxidative stress resistance of southwestern white pine (Pinus strobiformis Engelm.) were studied in response to warming treatments applied during embryogenesis, germination, and early seedling growth. Daytime air temperature surrounding cones in tree canopies was warmed by +2.1°C during embryo development. Resulting seeds and seedlings were assigned to three thermal regimes in growth chambers, with each regime separated by 4°C to encompass the wide range of temperatures observed over space and time across the species' range, plus the effect of heat waves coupled with a high carbon emissions scenario of climate warming. The embryo warming treatment reduced percent seedling emergence in all germination and growth environments and reduced mortality of seedlings grown in the warmest environment. Warm thermal regimes during early seedling growth increased subsequent seedling resistance to oxidative stress and transpirational water use. Experimental warming during seed development, germination, and seedling growth affected seedling emergence and survival. Oxidative stress resistance, morphology, and water relations were affected only by warming imposed during germination and seedling growth. This work explores potential outcomes of climate warming on multiple dimensions of seedling performance and uniquely illustrates that plant responses to heat vary with plant developmental stage in addition to the magnitude of temperature change.

Evaluation of policies as drivers of energy transition and modeling of a low carbon emission scenario for the energy generation sector in Kenya

Energy transition is considered integral in seeing countries reduce their emissions and thus reduce the effects of global warming. Understanding the energy, food, and climate change nexus is important for the achievement of a green economy. This paper examines how energy transition plays a key role in the shift to a low-carbon economy and address greenhouse emissions in the country. The research objectives that this paper seeks to answer are (1) how the existing energy- climate change policies and legal frameworks are influencing renewable energy technology adoption and (2) how the increase in renewable energy in the energy mix impacts carbon emission and achieving the national determined contribution goal. Data were collected through interviews from identified key informants. The findings showed that government involvement through policies has significantly led to the increase in adoption of renewable energy technologies in the country. It was determined that clean energy technologies across the country have increased to 14% by 2016 after the implementation of various energy policies. Additionally, the increase of renewable energy sources in the national generating mix is modeled to have a significant reduction in greenhouse emissions in the country. The low-carbon scenario model estimates that emissions would be reduced to 0.1 metric tonnes by 2040 based on the government’s energy target plans. It was concluded that clean and affordable energy is vital but there is a need for public consultations and awareness of new infrastructure that come with energy transition as this plays a key role in the achievement of sustainable development goals in Kenya.

Assessment of changes in the ichthyofauna in a tropical reservoir in south‐eastern Brazil: Consequences of global warming?

AbstractIncreases in temperatures over the last century, more intensively after the eighties, were recorded in several databases for the south‐eastern Brazil. These increases are likely to change fish communities in aquatic systems by decreasing abundances, biomass and sizes of some species more sensitive to climate change. Reservoirs may be particularly susceptible to the effects of climate change, as they isolate previously connected areas limiting fish dispersal and migration, as well as increasing water temperature and thermal stratification. We assessed temporal changes in the abundance and biomass of the ichthyofauna in an isolate reservoir (inflowing waters from small tributaries in a highland region) aiming to associate changes with climate effects over three decades (1990, 2000, 2010). Two abundant native species, a loricariid Loricariichthys castaneus (Castelnau, 1855) and a cichlid Geophagus brasiliensis (Quoy &amp; Gaimard, 1824), were selected to assess eventual response to climate changes in their geographical distribution in the Southeast Atlantic Hydrographic Region, considering different carbon emission scenarios using ecological niche models. A decrease in the densities and biomass of the ichthyofauna and of the two selected species were observed in both summer and winter over the three decades, coinciding with increases in temperatures. These decreases were correlated with increases in positive anomalies of temperature, which may be an indicator of climatic changes, although with low explanation of the data variation. The mean size of L. castaneus decreased between the nineties and 2010, whereas no significant trend was found for the size of G. brasiliensis. Our predictions of new area for future adequacy indicated a loss in the distribution area for both species (mainly for G. brasiliensis), considering the most pessimistic scenario for 2050 and 2070, possibly due to climatic changes. Both hydroelectric dams and global warming pose threats to freshwater fish diversity, and both will interact. Changes observed in the fish assemblage over the last decades were essential for an assessment of the consequences of global warming in this type of reservoir combining larger scale studies with future projections.