Energy Efficiency: Towards the End of Demand Growth

Top-down models, however, have difficulty forecasting long time horizons, when the base assumptions upon which the model was built have changed for example, rapidly accelerating population growth, transformational technologies and so on Furthermore, top-down models do not subdivide end-use types, which makes it difficult to identify areas for improvement or to understand the physical and behavioural drivers of energy consumption.

In contrast, bottom-up models calculate the energy consumption of a subgroup of buildings and then extrapolate to represent the entire building sector. Statistical bottom-up models utilize historic relationships between energy consumption and building end-uses to develop mathematical relationships among the parameters 18 , whereas engineering bottom-up models calculate the energy consumption of the end-uses based on the equipment in the buildings without any historic information The advantage to bottom-up approaches is that end-uses can be directly predicted and targeted for improvement, at the disadvantage of having much greater complexity, data requirements and computation time.

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To avoid this limitation, many models used for long-term forecasts employ a top-down approach An emerging group of studies have applied bottom-up archetype and statistical models for climate forecasting 31 , 32 , 33 , 34 , 35 , An advantage of these studies is that most preserve building end-uses, which is useful for policy recommendations.

The majority of these studies simplify the building characteristics to be single zone spaces, estimating thermal loads by balancing heat transfer equations, and do not capture the heterogeneity of the building stock. In addition, the climate change analysis is often a sensitivity analysis to temperature instead of a forecast of future conditions.

Climate change is incorporated from a statistically downscaled general circulation model GCM.

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For this study, we develop archetype-based bottom-up engineering models to forecast electricity and natural gas NG consumption between and in the residential sector in LAC. This type of modelling creates a group of building energy simulations that represent the entire building stock. This is the most appropriate model for the project goals as an archetype model is not bound by historic data trends, allows transparency and manipulation of end-use types and allows for the customization of the model to LAC.

A drawback to this approach is the high computational resources required to run the building simulation ensemble as well as the detailed data input requirements. We overcome this by simplifying the simulation where possible and utilizing several data sources on building configuration and inhabitant behaviour available for LAC. A bottom-up model is necessary to maintain end-use consumption detail, which we then modify for future years to investigate the impact of energy efficiency improvements across the building stock.

An advantage of our modelling over previous efforts is that we utilize a large number of archetypes to capture the heterogeneity of the building stock and also use multiple GCMs and climate scenarios to capture a range of potential climate outcomes. We therefore recommend aggressive energy efficiency, in combination with low-carbon generation sources to offset projected increases in residential energy demand. Scenario 1 represents an energy future with minimal efficiency increases beyond current policies, and Scenario 2 represents a future that incentivizes heavy electrification of water and space heating without improving efficiency beyond Scenario 1.

Under the most optimistic climate, RCP 2. We develop these two cases to represent the upper and lower bounds of potential energy savings. In these scenarios, increasing demand is driven by 1 increased adoption of cooling equipment, 2 increased use of cooling equipment as average temperatures increase, 3 population growth and 4 moderate increases in plug loads. These increases are comparable to previous energy forecasts for LAC Electricity demand is represented by solid lines and natural gas demand by dashed lines note: The grey area shows the variability in our forecast due to the different general circulation models.

Our results show that, while BAU Scenarios 1 and 2 forecast increases in total energy use under all RCPs, accelerating the adoption of efficient appliances Scenarios 3 and 4 can offset some or most of the increased demand Fig. In the two mitigation scenarios under RCP 8.

Key Findings

In Scenario 4 under RCP 2. This suggests that energy efficiency could be a viable resource for mitigating increases in energy use. Full results for total demand are in Supplementary Tables 1—8. To ensure reliability, electrical grids must be designed to meet highest demand periods that occur in hot summer months, and our simulations show that the peak demand could more than triple under RCP 8. Furthermore, previous studies point out that the statistically downscaled weather forecasts tend to underestimate peak temperature increases 39 , which means that increases in maximum annual peak demand might be even greater than what is captured in our model.

Unlike NG, which is available on-site and on-demand, electricity provision is met from capacity across states, which must be planned in advance and actively managed so that supply can continually meet demand. A scenario with large-scale fuel-switching between electricity and NG could mean an expansion of electricity infrastructure, potentially including the addition of expensive generation stations. A more cost-effective approach might be investing in efficiency; in RCP 8. Efficiency can save money for homeowners 40 and avoid costly upgrades for utility companies 41 , For in Scenario 2 under RCP 8.

Here we display the maximum annual electricity demand — and the number of hours per year with an electricity demand above the maximum of The coloured lines are the average values across all models and the grey shading is the variability due to differences in the GCMs. The spatial distribution of electricity demand increases is not uniform throughout LAC and depends upon climate zone, age of building and appliances, and consumption behaviour differences of inhabitants Fig.

The largest percent increases are located in the central inland areas with higher population density Supplementary Fig. Inland regions experience the highest increases in temperatures and subsequent energy use under higher RCPs, but under low RCPs these are the areas where net savings could be realized. Location-specific efficiency upgrades, especially with regards to building shell improvements and heating and cooling equipment upgrades, could be an effective pathway to maximize energy reduction per dollar invested in efficiency programmes.

Spatial results are tabulated in Supplementary Tables 9— Each map displays the average per cent change in residential electricity demand for the CBG between and under a specific RCP and mitigation scenario. Increases are represented in shades of red and decreases are represented in shades of blue. Our results show that the majority of projected electricity increases can be offset and net energy use can decline through the aggressive application of energy-efficient technologies.

Whereas electricity consumption varies significantly under different RCPs and scenarios, NG consumption is declining in all cases. Within our model, we include all RCPs to represent the potential variability in future climate, but recent studies suggest that current carbon emissions trends most closely follow RCP 8. Space conditioning is a major driver of the increases in electricity consumption in Scenario 2 due to both increased air-conditioning saturation and the electrification of heating technologies.

Our results indicate that directly targeting these increases through efficiency could be an effective means of mitigating electricity increases. Although we focus our research on demand forecasting, increasing prevalence of renewable sources in the energy supply has significant potential to offset projected increases in demand, particularly at peak hours, as well as to alter consumer behaviour. Although distributed photovoltaics PV are excluded from this goal, California will likely continue to provide incentives such as the California Solar Initiative for PV in order to reach its greenhouse gas goals.

An advantage to PV is that, generally, times of heavy electricity production that is, when the sun is shining tend to coincide with periods of high electricity demand driven by heavy air-conditioning use. Because of this, PV has the potential to reduce the load on the electric grid during mid-afternoon peaks and during heat waves, which are also times that PV is most reliable. In a situation where a consumer has abundant solar electricity during the day, behaviour might change to shift consumption into the evening hours, exacerbating this new evening peak.

Electric vehicles also could be charged in the evening or overnight. These behavioural changes could cause households to consume more energy than in a situation with no solar panels, but some of the additional strain on the electricity grid would be eased regardless, as the afternoon peak could be eliminated, potentially avoiding the installation of new centralized generation facilities. PV could be an effective strategy combined with the energy efficiency measures explored in this paper to prepare for future energy consumption under climate change.

Furthermore, other renewable technologies such as solar water heating or on-site electricity storage could be effective pathways to meeting future energy needs. Solar water heating was included in our technology options, but we did not consider large-scale shifting from traditional fuel sources for water heating that is, electricity and NG that could potentially lower the demand for water heating significantly.

Aggressive building upgrades and appliance efficiency improvements have the potential to offset projected increases in energy demand. If large-scale fuel-switching between electricity and NG occurs, it will additionally be imperative to reduce consumption as electricity supply could be constrained. Targeting energy efficiency would be most effective if coupled with supply-side strategies such as distributed PV.

According to our simulations, in the heating and cooling sector existing technologies could be enough to substantially offset demand increases from fuel-switching; however, timely intervention is necessary to ensure near universal adoption as annual temperatures continue to climb LAC will have to continually phase-out older technologies and raise the standard for minimum acceptable efficiency, and this will need to be done in stages to avoid large energy and appliance purchase cost increases for consumers.

In other appliance categories, aggressive efficiency upgrades will require technology innovation that significantly improves upon commercially existing models. Implementation of policies having an impact on these efficiency upgrades will need to happen within the next decade if these aggressive goals are to be met. If such investments are made at the same time as de-carbonizing and improving the resiliency of the energy supply, there could be co-benefits that could significantly lower the greenhouse gas emissions of residential power consumption and reduce costs to consumers.

Future research should focus on quantifying the linkages and feedback loops between electricity supply and demand in the presence of renewable energy sources and understanding the cost of implementing different initiatives. To quantify the relationship between energy consumption and climate change, we develop a model for forecasting residential energy use between and in LAC. Our model is a spatially and temporally resolute bottom-up assessment of residential energy use, which we calibrate against actual consumption data Using survey data and physical information about the building stock, we create 84 archetype-building simulations in the Building Energy Optimization BEopt software to represent all residential buildings in LAC.

In BEopt, we utilize EnergyPlus, a state-of-the-art building simulation software developed by the US Department of Energy, as the main simulation engine. We subdivide the archetypes based upon year of construction, classification that is, single family detached, townhouse and so on and climate zone. For each archetype we include 21 heating and 13 cooling technologies Supplementary Table Next, we scale electricity and NG consumption to the county level while maintaining spatial detail by census block group CBG.

An assumption of the model is that patterns of use are correlated to building type, for example, that homeowners in similar vintage homes in the same climate zone will use similar set temps within their homes. We then forecast residential electricity and NG consumption in LAC under climate change and increasing population. We also develop scenarios of varying appliance and building efficiency to investigate the possibility of offsetting projected energy increases.

In developing the archetypes, we utilize three major sources of information: RASS is a California-specific appliance survey administered by the California Energy Commission that captures a diverse set of variables on building thermal properties and appliance use. The LAC Assessor's office maintains a database of every building standing in LAC 50 , primarily for tax purposes, and we utilize their information on building size, classification, location and quality in developing the archetypes.

We use the handbook as a complement to the Assessor's database and RASS to add in additional details on thermal properties for each. We give a summary of the data from each source in Table 2.

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In LAC, the climate varies greatly between coastal and inland regions; therefore, we differentiate archetypes based upon five climate zones Supplementary Note 1 and Supplementary Table We develop custom building archetypes based upon our previous work 51 , which were then subdivided by climate zone, period of construction and residential building type. This results in a total of 84 archetypes Table 3. We next group all of the residential buildings of LAC in the Los Angeles Assessor database into each of the 84 categories.

We use characteristics from the Assessor database as specifications for the archetypes for example, average building size , and the grouping also allows for the final simulation results to be scaled to the county level. For each of the 84 categories, we compiled a profile of the typical building shape perimeter to area ratio , predominant material in the framing, average size and quality class from the Assessor database Table 1.

California assessors use the quality class designation to indicate greater quality and home value. In some cases, this means improved thermal properties as well. For archetypes that are in the same climate zone that have the same predominant quality class and similar floor areas, we combine them to save on computation time. We maintain all 84 archetypes for appliance assessment, but for the building simulations we use a condensed 51 simulation models Table 4.

For these 51 categories, we develop models in BEopt using data from the three sources on the thermal properties of the building Table 1. For HVAC technologies, we use 21 different heating technologies and 13 different cooling technologies within each archetype Supplementary Table However, if we only simulate this one technology for all archetypes, we would fail to capture the true variability in heating technologies and associated energy use that exists. Instead, we run all of the technologies in BEopt and weight the energy consumption by archetype category based on the RASS survey responses.

BEopt output is in hourly increments as is the BEopt core simulation engine, EnergyPlus , and we aggregate this to yearly resolution for the calibration to be consistent with the LADWP data. To get the total consumption for the residential building stock, we normalize HVAC end-use consumption per square foot by archetype, and multiply the square footage of each archetype category within each CBG.

In addition, we simulate lighting with the 51 simulation archetypes and normalize per square foot, but we maintain appliances at the per archetype level. In the aggregation, appliance types were maintained so that end-use could be ascertained in the final model and tracked in the forecast. We run the simulation ensemble for — and develop custom weather files for BEopt for LADWP climatic conditions to be commensurate with the calibration data set. Researchers at the University of California, Los Angele obtained these data as part of a research project with the California Energy Commission BEopt utilizes an EnergyPlus Weather EPW file, which includes a range of climatic variables such as temperature, humidity, solar radiation, snow cover, precipitation and rainfall Supplementary Table We create a custom EPW for each of the five climate zones for this time period, utilizing climatic inputs from local weather stations 52 and publicly available solar radiation databases Once we run the ensemble with the appropriate weather data and scale it to county level, we are able to extract the subset of CBGs that exist within the LADWP service area.

The goals of the calibration are to 1 have the total modelled electricity consumption be equivalent to the LADWP reported consumption and 2 to have the end-use consumption percentages in the model similar to those reported in RASS. To identify archetypes where thermal properties need to be adjusted, we compare normalized heating and cooling electricity consumption by archetype from the model to the end-use consumption reported by RASS for that archetype. RASS models rather than measures end-use consumption, but this is still useful for identifying which archetypes are above or below the expected value.

To prioritize which archetypes to modify, we then weight the deviation from RASS by the average total floor area of all buildings mapped to that archetype. Priority is given to archetypes with high coverage by floor area since they have the largest influence on the model. Once we identify the archetypes for modification, we modify thermal properties of the shell within the uncertainty bounds of the input data sources, for example, changing duct efficiency, changing flooring or increasing insulation.

For appliances, rather than adjusting the distribution of types within homes, we use linear scaling factors to adjust consumption towards the expected end-use breakdown. The entire calibration procedure is based upon electricity consumption since that are the data that we have for validation, but NG comprises a significant amount of energy use in LAC residential consumption, mostly in water and space heating. NG data are not available for calibration, but we maintain NG results to compliment the electricity modelling. Final calibrated end-use consumption for the base year is located in Supplementary Table These files are the weather data input for the building simulation software, BEopt.

This can then be used as a standard for predicting and comparing building performance in a single location, given the assumption that the climate of the location is not changing. The Intergovernmental on Climate Change's fifth assessment report utilizes four different projections of atmospheric carbon concentrations known as RCPs. Each RCP was developed by an independent modelling team and is designated by their year radiative forcing level. For example, the most optimistic scenario RCP 2.

The most pessimistic scenario, RCP 8. In this study, we use 10 GCMs for each of the four RCPs to capture a range of future climate scenarios that could have an impact on residential energy consumption. To maintain spatial differentiation between the climate zones, we utilize statistically downscaled CMIP5 via bias correction with constructed analogues projections for temperature.

To obtain a representative temperature forecast for each model run and climate zone, we take all grid points from that run within a climate zone and average them at every point in time. We morph the temperature trajectories for each model run, using the modification of Sailor 60 to Belcher's originally proposed method:.

For our study, the base weather files are the EPWs developed by the CEC for each of the 16 climate zones in the state of California and available as default files for BEopt. Effectively, this morphing transformation matches the maximum and minimum daily temperatures from the GCM and scales the intermediate hours based on the EPW pattern. For each morphed temperature trajectory, we use a 4-h weighted average to smooth discontinuities between days. These files can then be run with our 51 calibrated archetypes. Tracking the population growth with Southern California Association of Government's housing forecast through which includes changing household size , we develop bi-decadal housing growth rates for LAC Supplementary Table We apply these housing growth rates based on population to all the scenarios, starting with the building stock from the Assessor's database.

Energy efficiency : towards the end of demand growth (Book, ) [www.newyorkethnicfood.com]

In a previous study, we assessed historic building turnover trends in LAC, and developed a model of building turnover based upon initial year of construction In addition to housing growth, we include these building turnover rates in the stock model by replacing older vintages with newer vintages of the same classification and climate zone.

For Scenarios 1 and 2 we utilize the same rates as in our previous paper, and in Scenarios 3 and 4 we augment the turnover to be 10 times the natural rate to represent incentives for building turnover and building shell upgrades Table 5. We did not model building shell upgrades individually for example, improved windows, insulation and so on , but instead we use augmented turnover as a proxy for shell upgrades since newer archetypes have more efficient thermal shells.

In applying the population growth and building turnover, we spatially distribute the changes based upon the location of existing dwelling units. For population growth, in reality, new construction might be more likely to occur in less populated CBGs rather than densifying existing areas.

We simulate all archetypes under 10 GCMs and 39 types of heating and cooling equipments, and then we average the results to obtain a mean prediction of total energy with the differentiation in the GCMs representing the variability of the forecast. We also maintain spatial resolution in our simulation ensemble to investigate spatial differences in changing energy demand under climate change. Utilizing the custom weather files we developed for each of the GCM runs, we simulate our 51 archetype models using batch processing for EnergyPlus.

EnergyPlus is the main simulation engine for BEopt; therefore, once we create the models, we can customize the EnergyPlus input files and directly run them in EnergyPlus. This saves on processing time and allows us to customize the simulation output format. We perform a total of 83, simulations: With the model output, we post process the data with Python and store it in a SQLite database.

In the archetype calibration, we run the models with 28 different heating and cooling technologies, and for the forecasting we included additional 11 technologies Supplementary Table To run the full ensemble, this would result in 3,, building simulations, which is computationally limiting. We utilize these factors in post processing to compute energy consumption for each of the 3,, cases without having to run each of the simulations. Similar to the calibration phase, we calculate a weighted average of HVAC technologies based on the number of dwelling units and the prevalence of the technology within each archetype category.

The main difference is that these factors are temporally dynamic in the forecast since technology adoption and the number of dwelling units change over time. The maximum and minimum energy values across all the models for each year are the uncertainty bounds for that RCP. Within the forecast, we evaluate appliance technology adoption and efficiency gains to test the potential to offset increases in demand. We develop four scenarios to test with each of the climate predictions: These variables are dynamically changed within each archetype category for each year between and so that there is a distinct forecast for electricity and NG in each hour over this time period.

Scenario 1 includes existing and proposed policies and normal building and appliance turnover. Scenario 2 includes the same assumptions except that all retired water and space heating equipment, regardless of original fuel type, is replaced with an electric version. Scenario 3 includes heavy electrification as in Scenario 2 , but adds in moderate efficiency gains in appliances beyond existing standards and increased building turnover.

Scenario 4 also starts with heavy electrification, but includes efficiency and building gains beyond current technologies. In all cases, we include an increased rate of air-conditioning saturation that is, the proportion of dwelling units with air conditioners , as previous research finds that saturation correlates strongly with temperatures 1. Our mitigation strategies are based upon augmented versions of existing policies at the state and federal level and apply to both electric and NG appliances.

As part of our analysis, we estimate the cost of conserved energy. The full background can be found in Supplementary Note 2. In the next section, we provide an overview of the assumptions underlying each scenario including the current policies and publications that support the development of these assumptions.

We summarize efficiency measures in Table 6. We turnover existing heating and cooling equipment within each archetype category using a distribution based on the age of the equipment ex: These turnover time frames are constant for all scenarios. We then create a replacement matrix that gives a distribution of technologies that replaces any retired equipment of each category. In Scenario 2, we also use the purchase trends, but remove NG and propane as choices for new appliance replacement and instead distribute new purchases only among electric technologies.

In Scenario 3, we restrict all heating replacements to be only heat pumps, and in Scenario 4 all heating and cooling equipments are replaced by only the most efficient heat pumps in the model. In addition to the retirement of aging HVAC equipment, we also add in additional cooling equipment based upon previous studies on saturation rates of air conditioning and temperature. Sailor and Pavlova 1 developed an empirical relationship between CDD and per cent saturation of air conditioning based upon data from cities throughout the United States. In this equation, S y is per cent air-conditioning saturation in a given year, S is the initial saturation, CDD is the cooling degree days of the future year and CDD is the initial number of CDD.

We apply this relationship for every climate zone and every year for all four average RCPs to obtain saturation rates for every year. Since CDD can be somewhat variable from year to year in the forecasts, we forward fill saturation rates so that an air-conditioning saturation rate in a future year cannot be less than in a previous year. For example, if projects fewer CDD than , we apply the saturation rate to since those who purchased air conditioners in will not discard them the next year.

We then apply the saturation rates to archetypes for that climate zone in that year. With the exception of lighting and plug loads, we assume the number of appliances utilized is linearly proportional to the number of dwelling units. This section discusses the per unit changes in appliances, not to the total amount consumed by each category. In , the lighting portion of the Energy Independence and Security Act went into effect regulating the power consumption of incandescent light bulbs in the United States Beyond increasing the efficiency of incandescent light bulbs, this has driven down the cost of alternative light bulbs such as compact fluorescent lighting and light-emitting diodes LEDs.

The CEC has proposed regulations on computers and monitors that would take effect in and This rule would set performance standards for laptops, desktops and monitors as well as target standby energy consumption We include nine different types of water heaters with four different fuels electric, gas, propane and solar. But stringent fuel-efficiency measures for cars and trucks, and a shift which sees one-in-four cars being electric by , means that China is no longer the main driving force behind global oil use — demand growth is larger in India post A bcm increase in US shale gas production over the 15 years from would comfortably exceed the previous record for gas.

Expansion on this scale is having wide-ranging impacts within North America, fuelling major investments in petrochemicals and other energy-intensive industries. It is also reordering international trade flows and challenging incumbent suppliers and business models. Up until the mids demand growth remains robust in the New Policies Scenario, but slows markedly thereafter as greater efficiency and fuel switching bring down oil use for passenger vehicles even though the global car fleet doubles from today to reach 2 billion by Once US tight oil plateaus in the late s and non-OPEC production as a whole falls back, the market becomes increasingly reliant on the Middle East to balance the market.

There is a continued large-scale need for investment to develop a total of billion barrels of new resources to , mostly to make up for declines at existing fields rather than to meet the increase in demand. Even greater upside for US tight oil and a more rapid switch to electric cars would keep oil prices lower for longer. We explore this possibility in a Low Oil Price Case, in which a doubling of the estimate for tight oil resources, to more than billion barrels, boosts US supply and more widespread application of digital technologies helps to keep a lid on upstream costs around the globe.

Extra policy and infrastructure support pushes a much more rapid expansion in the global electric car fleet, which approaches million cars by However, it is not sufficient to trigger a major turnaround in global oil use. Even with a rapid transformation of the passenger car fleet, reaching a peak in global demand would require stronger policy action in other sectors. Otherwise, in a lower oil price world, consumers have few economic incentives to make the switch away from oil or to use it more efficiently.

Meanwhile, with projected demand growth appearing robust, at least for the near term, a third straight year in of low investment in new conventional projects remains a worrying indicator for the future market balance, creating a substantial risk of a shortfall of new supply in the s. Natural gas grows to account for a quarter of global energy demand in the New Policies Scenario by , becoming the second-largest fuel in the global mix after oil. In resource-rich regions, such as the Middle East, the case for expanding gas use is relatively straightforward, especially when it can substitute for oil.

In the United States, plentiful supplies maintain a strong share of gas-fired power in electricity generation through to , even without national policies limiting the use of coal. This reflects the fact that gas looks a good fit for policy priorities in this region, generating heat, power and mobility with fewer carbon-dioxide CO 2 and pollutant emissions than other fossil fuels, helping to address widespread concerns over air quality. But the competitive landscape is formidable, not just due to coal but also to renewables, which in some countries become a cheaper form of new power generation than gas by the mids, pushing gas-fired plants towards a balancing rather than a baseload role.

Efficiency policies also play a part in constraining gas use: A new gas order is emerging, with US LNG helping to accelerate a shift towards a more flexible, liquid, global market. Ensuring that gas remains affordable and secure, beyond the current period of ample supply and lower prices, is critical for its long-term prospects. Gas supply also becomes more diverse: Price formation is based increasingly on competition between various sources of gas, rather than indexation to oil. With destination flexibility, hub-based pricing and spot availability, US LNG acts as a catalyst for many of the anticipated changes in the wider gas market.

The new gas order can bring dividends for gas security, although there is the risk of a hard landing for gas markets in the s if uncertainty over the pace or direction of change deters new investments. Over the longer term, a larger and more liquid LNG market can compensate for reduced flexibility elsewhere in the energy system for example, lower fuel-switching capacity in some countries as coal-fired generation is retired.

Universal access to electricity remains elusive, and scaling up access to clean cooking facilities is even more challenging. There are some positive signs: Progress in India and Indonesia has been particularly impressive, and in sub-Saharan Africa electrification efforts outpaced population growth for the first time in Household air pollution from these sources is currently linked to 2.

Policy attention to air quality is rising and global emissions of all the major pollutants fall in our projections, but their health impacts remain severe. Ageing populations in many industrialised societies become more vulnerable to the effects of air pollution and urbanisation can also increase exposure to pollutants from traffic. Premature deaths worldwide from outdoor air pollution rise from 3 million today to more than 4 million in in the New Policies Scenario, even though pollution control technologies are applied more widely and other emissions are avoided because energy services are provided more efficiently or as with wind and solar without fuel combustion.

This outcome is far from enough to avoid severe impacts of climate change, but there are a few positive signs. However, the speed of change in the power sector is not matched elsewhere: The Sustainable Development Scenario offers an integrated way to achieve a range of energy-related goals crucial for sustainable economic development: This scenario starts from a set of desired outcomes and considers what would be necessary to deliver them.

A key finding is that universal access to electricity and clean cooking can be reached without making this task any more challenging. Electric cars move into the mainstream quickly, but decarbonising the transport sector also requires much more stringent efficiency measures across the board, notably for road freight.

The targets for renewables and efficiency that are defined in the Sustainable Development agenda are met or exceeded in this scenario; renewables and efficiency are the key mechanisms to drive forward the low-carbon transition and reduce pollutant emissions.