Energy Policy Decisions Research Articles

Probabilistic modeling of future electricity systems with high renewable energy penetration using machine learning

The increasing penetration of weather-dependent renewable energy generation calls for high-resolution modeling of the possible future energy mixes to support the energy strategy and policy decisions. Simulations relying on the data of only a few years, however, are not only unreliable but also unable to quantify the uncertainty resulting from the year-to-year variability of the weather conditions. This paper presents a new method based on artificial neural networks that map the relationship between the weather data from atmospheric reanalysis and the photovoltaic and wind power generation and the electric load. The regression models are trained based on the data of the last 3 to 6 years, and then they are used to generate synthetic hourly renewable power production and load profiles for 42 years as an ensemble representation of possible outcomes in the future. The modeled profiles are post-processed by a novel variance-correction method that ensures the statistical similarity of the modeled and real data and thus the reliability of the simulation based on these profiles.The probabilistic modeling enabled by the proposed approach is demonstrated in two practical applications for the Hungarian electricity system. First, the so-called Dunkelflaute (dark doldrum) events, are analyzed and categorized. The results reveal that Dunkelflaute events most frequently happen on summer nights, and their typical duration is less than 12 h, even though events ranging through multiple days are also possible. Second, the renewable energy supply is modeled for different photovoltaic and wind turbine installed capacities. Based on our calculations, the share of the annual power consumption that weather-dependent renewable generation can directly cover is up to 60% in Hungary, even with very high installed capacities and overproduction, and higher carbon-free electricity share targets can only be achieved with an energy mix containing nuclear power and renewable sources. The proposed method can easily be extended to other countries and used in more detailed electricity market simulations in the future.

Macroeconomic and Uncertainty Shocks’ Effects on Energy Prices: A Comprehensive Literature Review

GDP, monetary variables, corruption, and uncertainty are crucial to energy policy decisions in today’s interrelated world. The global energy crisis, aggravated by rising energy prices, has sparked a thorough analysis of its causes. We demonstrate the significance of categorizing research by influence channels while focusing on their implications for energy policy decisions. We investigate the growing number of studies that use GDP, inflation, central banks’ characteristics, corruption, and uncertainty as critical factors in determining energy policies. Energy prices fluctuate because energy policies shift the supply–demand equilibrium. We categorise the effects and show that GDP, economic policy uncertainty, and, most notably, specific economic conditions and extreme events play a significant role in determining energy prices. We observed that energy consumption, GDP growth, and energy prices have a bidirectional, causal relationship. Still, the literature has not established which causative direction is the most significant. Taxes, interest rates, and corruption also significantly determine energy prices, although the origins of corruption have not been adequately examined. Lastly, uncertainty generally increases energy costs, but this relationship requires additional research in terms of the features of countries, conditions, and, most importantly, the theoretical backgrounds used.

The Politics of Policy Making: City Councils and Energy Policy Decision-Making

The Politics of Policy Making: City Councils and Energy Policy Decision-Making

Life cycle assessment of prospective scenarios maximizing renewable resources in the Brazilian electricity matrix

The energy transition from fossil to renewable sources is of paramount importance to reduce GHG and air pollutants emissions from energy generation processes while promoting socioeconomic development. This study investigates the long-term changes in prospective scenarios' environmental and economic performance in Brazil's electricity matrix. The reference Scenario represents the government's Energy Research Office (EPE) energy expansion plan, and in addition three different scenarios were developed. All of them assumed that fossil fuel use would be constant and equal to the values in 2019. In Scenario 2, we assumed that fossil fuel growth from the EPE plan would be replaced by maximum biomass penetration in the 2030-2050 timeframe. In Scenario 3 this replacement will be made by the maximum penetration of the wind source while in Scenario 4 by maximum solar photovoltaic source penetration. The best performance in terms of lower GWP is to Scenario 3, with 0.121 kgCO2/kWh. The lowest LCOE is reached in case of Scenario 3, at 0.0438 USD/kWh. Based on the obtained results the renewable sources evaluated in this study are advantageous in bringing environmental and economic benefits in Brazil, even in relatively small fractions. The results can be a support for energy policy decision-makers.

Spatial Mapping of Jamaica’s High-Resolution Wind Atlas: An Environmental-Sociotechnical Account

To utilize wind energy, how it works, its value, and where the best locations are for extrapolation must be understood. A high-resolution wind atlas of Jamaica aids the understanding of the sociophysical phenomena leading to a better understanding of wind energy on the island. This study incorporates a mesoscale method with eight years of relevant data in ArcGIS 10.8.1 to derive then indicate sites for potential onshore wind power plants (WPP). It uses secondary and real-time data from domestic and international sources to evaluate economic, environmental, and sociotechnical criteria. The results indicate a high possibility for future wind power (WP) generation expansion since 2867.15 km2, 26% of the land is available. With the installation of Vestas V80 turbines, 62,818.71 GWh/year can be generated. Conversely, Vestas V112 turbine installation can produce 56,321.74 GWh/year of electrical energy. The average speed goes up to 12.5 miles per hour, while the power density of the 10% windiest areas is between 156.60 and 768.37 W/m2 at 50 m above ground, with several parishes having appropriate locations for WPPs. Thus, 29-point sites are identified in the study. However, St. Elizabeth and Manchester are most favorable, with mean wind speeds of 8.26 m/s and 10.08 m/s, respectively, in the excellently suitable zones. The research offers several advantages, which encompass the quantification of wind potential with and without prohibition, assessment of wind suitability on the island of Jamaica, reduction in environmental damage, and available data amelioration to aid better energy policy decisions, which will ensure a faster and easier transition from fossil fuel (FF) to renewable energy (RE) to meet Jamaica’s 2030 50% RE generation target. Specifically, the atlases will assist policymakers and WP developers in making informed decisions by reducing costs, time, and ambiguities to enhance the development of renewable energy use for electrical energy in Jamaica. The Geographical Information System (GIS), which is one of the most popular energy assessment tools, was utilized to derive suitable land zones of 24.41 to 26% for onshore wind farm development in Jamaica. It incorporates environmental, economic, social, safety, and technical criteria with underlining categorical variables as indicators to derive the quantitative values appropriate for Jamaica’s landscape and comparable to international studies with similar objectives. It found that unrestricted areas can theoretically generate up to 62,818 GWh per year of electrical energy.

Racing to the last barrel: Linking oil and gas industry interests to climate inaction in Canada

Despite increasing urgency of the climate crisis, Canada has systematically failed to reach its greenhouse gas emissions reduction targets. Meanwhile, the country's fossil fuel industry, mostly based in the province of Alberta, has continued to grow, becoming the nation's main source of emissions. Recently, several studies have used a policy network (PN) approach to outline this industry's reach within Canadian public and political spheres. This research suggests that this network is one of the primary drivers of climate inaction in the country. While these studies outline the extent of the fossil fuel PN in Canadian society, further research is required to fully understand its degree of influence within Canadian politics. Particularly, it remains unclear how this network can steer specific policies towards preferred outcomes. As such, this study seeks to determine whether climate and energy policy decisions in Alberta reflect the interests of the fossil fuel industry. To do so, it observes the evolution of non-state actor membership in 11 key provincial policymaking bodies along with climate and energy policy change between 1990 and 2019. This analysis is also complemented by ten interviews of key individuals with decades of involvement in environmental or energy policymaking. The main findings show that fossil fuel industry actors have had substantial political influence on climate and energy policy decisions in Alberta for nearly three decades – and despite varying degrees of inclusion of other actors with differing interests. These findings help understand how industry interests can affect climate policy decisions in Canada.

Analysis of Price and Incentive Based Demand Response programs on Unit Commitment using Particle Swarm Optimization

The increase in power demand by various infrastructural development activities and industrial automations in recent years have made a vital effect with respect to the load demand. To effectively manage the load demand, several Load Management (LM) techniques has been adopted in all energy policy decisions. In the de-regulated power system, the Demand Side Management (DSM) owing to its advantages at economic environments are regarded as remarkable choice and has been extended to incorporate Demand Response Programs (DRPs) in the load management techniques. In this paper, a responsive load economic model is developed. This model is based on the two factors such as price elasticity of demand and welfare function of customers. A Demand Response (DR) based Unit Commitment (DRUC) problem is studied to execute the economic analysis of DRPs. The main idea behind the DRUC is to maximize the benefit of both customers and utilities. The Particle Swarm Optimization (PSO) technique has been used to resolve the unit commitment problem with and without DRPs. The optimization outcomes are also compared with the conventional methods. To verify the efficacy of the DRUC model, the conventional ten-unit test system is considered. The simulated result shows that Critical Peak Pricing (CPP) and Direct Load Control (DLC) gives the best peak reduction and Time of Use (TOU) gives the best total cost reduction among all the DRPs and the other conventional methods.

Climate impacts of fossil fuels in todays electricity systems

Oil, coal, and gas account for approximately 80% of global primary energy, but only a portion of total airborne CO2eq (approx. 35% at GWP20 to 65% at GWP100), even though they account for 95% of total measured CO2 emissions. The benefits of these energy sources, as well as their related costs, are not all incorporated in current energy policy discussions. Global greenhouse gas policies must include documented changes in measured airborne CO2eq to avoid spending large amounts of public funds on ineffective or sub-optimal policies. The authors examined airborne CO2, which is less than half of emitted CO2, as well as reported CH4 emissions and the global warming potential of CH4 as published by the IPCC for coal and natural gas. The surprising conclusion is that surfaced-mined coal appears 'better for the climate' than the average natural gas, and all coal appears beneficial over LNG. Therefore, current CO2-only reduction policies and CO2 taxes are leading to unintended consequences and the switch from coal to natural gas, especially LNG, will not have the desired impact of reducing predicted future global warming; in fact, quite the contrary. A large portion of anthropogenic global warming is attributed by the IPCC and IEA to CH4, but it must be noted that CH4 emissions from natural sources and from agriculture account for approximately 40% and 25% of annual global CH4 emissions respectively. Energy accounts for about 20% of documented CH4 emissions. CO2 contributes only approximately 35% of annual airborne anthropogenic GHG emissions after accounting for CH4, over a 20-year horizon. At a 100-year horizon, the contribution of CO2 increases to approximately 60%. Energy policies that do not consider all GHG emissions along the entire value chain will lead to undesired economic and environmental distortions. All carbon taxation and CO2 pricing schemes are incorrect and need to be revised. At IPCC's GWP20 an approximately 2%1 higher loss of CH4 across the value chain prior to combustion of natural gas versus coal will lead to 'climate parity' of coal with natural gas. According to public data, natural gas value chains have high CH4 and undocumented CO2 losses. On a global average, using only IEA-documented CH4 data, natural gas emits approximately 15% more CO2eq than surface-mined coal, over a 20-year horizon. This difference will increase as the use of shale gas and LNG expands. Investors should support all energy systems in a manner that avoids an energy crisis, including intermittent renewable energy systems where they make sense. If CO2 emissions need to be reduced, one of the most effective ways would be to install ultra-supercritical power plants with CCUS technology. However, the undisputed benefits of increased CO2 concentrations in the atmosphere because of its promotion of photosynthesis and plant growth effects (fertilization) need to be considered in energy policy decisions as well. The authors suggest that future research and development should concentrate on reducing net emissions from fossil fuel power plants and providing cost-effective and reliable new conventional power generation capacity, utilizing clean coal and clean natural gas technology.

Evaluation of Social Projects Using Neutrosophic AHP

Recently, industrialization has led to a worldwide rise in energy usage. Consequently, satisfying rising energy demands has assumed more significance. Fuel, gasoline, and nat gas are all finite resources, making it all the more important to discover sustainable energy alternatives. To fulfill the current need for energy, renewable resources play a significant role. Therefore, energy decisions and government policy are of paramount importance for nations. Energy policy and judgment challenges, such as the appraisal of energy projects, the choice among fuel sources, the location of power plants, and the determination of energy policy, are solved using a variety of technical, financial, ecological, and social factors. Multi-criterion decision-making (MCDM) methodologies may be used to assess energy policy decisions, one of the important challenges for governments. Some of the challenges associated with making energy-related decisions and formulating policies are choosing between various energy sources, assessing the relative merits of various energy supply techniques, formulating an energy strategy, and carrying it through. Various forms of fuel sources are taken into account in the much research that has been conducted on energy decision-making challenges. Because they take into account several, sometimes competing, criteria in their assessments of potential solutions, MCDM techniques have proven useful in the resolution of energy-related decision-making issues. By combining MCDM with the neutrosophic set theory (NST), which captures the inherent ambiguity of human judgment, we may get more nuanced, tangible, and practical outcomes. This work intends to provide a thorough analysis of the methodology and implementations of neutrosophic MCDM in the power industry, as well as to synthesize the current literature and the latest recent breakthroughs to help guide researchers in this area. The neutrosophic Analytic Hierarchy Process (AHP) method is used to compute the weights of each criterion of energy in a social project. This research shows that neutrosophic AHP, either on its own or in combination with another MCDM approach, is the most often used MCDM technique.

New Dynamics of Great-Power Energy Politics in South-Eastern Europe: The EU versus the US and Russia?

South-Eastern European (SEE) countries are typically keen to maintain the status quo in their energy systems, generally characterized by underinvestment, high coal share and utility affordability needs. Their energy mixes have historically been determined by external factors, currently mainly related to decarbonization pressure. This article assesses how the EU’s ongoing decarbonization-driven withdrawal from supporting natural gas projects shapes fuel choices in nine selected SEE countries and may have geopolitical consequences. It is based on more than 70 interviews with stakeholders from these countries, EU institutions, and international organizations. In exploring and theorizing the geopolitical ramifications of the energy transition in SEE, it applies a novel approach, which draws on theories of power and the concept of an assemblage, which we link to theories on entanglement and disentanglement. We find that the EU’s climate policy significantly changes local infrastructural assemblages and the EU’s disentanglement from natural gas goes against Russian and US efforts. By wielding its power to support such an energy transition, the EU has shifted the bipolar system ‘EU/US vis-à-vis Russia’ defined along a single geopolitical ruleset (supply security), to a tripolar disposition ‘EU-Russia-USA’ defined along two rulesets (supply security and climate policy). In addition, China has become involved. States will thus have to take crucial energy policy decisions in a new geopolitical context.

Building New Nuclear in Finland: Crises Challenging Core Beliefs around Nuclear Energy

This paper examines the building of a new nuclear plant as a greenfield operation in Pyhäjoki, Finland. A newly-founded energy company, Fennovoima, was granted a license by the Finnish parliament to build a new power plant in 2010. In the years following this Decision in Principle the project faced several obstacles. Through interviews with key actors, this paper identifies the interests and core beliefs associated with building the plant. It posits that, in Finland, the underlying ideas and values surrounding nuclear energy are deeply embedded in conventional belief, or ‘doxa’-like, and as such are very difficult to challenge or change. Through the lens of this example, it offers suggestions and conclusions concerning the more general social logic behind energy policy decisions in the Finnish context and why nuclear energy has become a hegemonic solution in the Finnish polity. The paper also discusses the viewpoint of geopolitics, which has had historical relevance and has become of paramount concern in the current world situation.

The Role of Discounting in Energy Policy Investments

For informing future energy policy decisions, it is essential to choose the correct social discount rate (SDR) for ex-ante economic evaluations. Generally, costs and benefits—both economic and environmental—are weighted through a single constant discount rate. This leads to excessive discounting of the present value of cash flows progressively more distant over time. Evaluating energy projects through constant discount rates would mean underestimating their environmental externalities. This study intends to characterize environmental–economic discounting models calibrated for energy investments, distinguishing between intra- and inter-generational projects. In both cases, the idea is to use two discounting rates: an economic rate to assess financial components and an ecological rate to weight environmental effects. For intra-generational projects, the dual discount rates are assumed to be constant over time. For inter-generational projects, the model is time-declining to give greater weight to environmental damages and benefits in the long-term. Our discounting approaches are based on Ramsey’s growth model and Gollier’s ecological discounting model; the latter is expressed as a function of an index capable of describing the performance of a country’s energy systems. With regards to the models we propose, the novelty lies in the calibration of the “environmental quality” parameter. Regarding the model for long-term projects, another innovation concerns the analysis of risk components linked to economic variables; the growth rate of consumption is modelled as a stochastic variable. The defined models were implemented to determine discount rates for both Italy and China. In both cases, the estimated discount rates are lower than those suggested by governments. This means that the use of dual discounting approaches can guide policymakers towards sustainable investment in line with UN climate neutrality objectives.

Best practices for analyzing the direct energy use of blockchain technology systems: Review and policy recommendations

There are a growing number of blockchain applications in energy systems, but surprisingly little is known about their direct energy demand outside of cryptocurrency applications. Addressing this knowledge gap should be a key policy priority so that the energy use of blockchain systems can be better understood and managed as applications proliferate. To help policy makers and energy analysts achieve this goal, this article makes three contributions. First, we present a brief review of blockchain system components, energy demand drivers, and emerging applications to provide a common conceptual foundation. Second, we propose best practices for energy analysis of blockchain systems and identify best practice lapses in previous literature that presently cloud our understanding of energy use. Finally, we propose priority research areas to address identified lapses and knowledge gaps, which would ensure future research produces data and estimates that are maximally relevant to energy policy decisions.

Cross-Validation of the MEDEAS Energy-Economy-Environment Model with the Integrated MARKAL-EFOM System (TIMES) and the Long-Range Energy Alternatives Planning System (LEAP)

In the present study, we compare energy transition scenarios from a new set of integrated assessment models, the suite of MEDEAS models, based on a systems dynamic modeling approach, with scenarios from two already well know structurally and conceptually different integrated assessment models, the Integrated MARKAL-EFOM System (TIMES) and the Long-Range Energy Alternatives Planning system (LEAP). The investigation was carried out to cross-compare and benchmark the response of MEDEAS models with TIMES and LEAP in depicting the energy transition in two different countries, Austria and Bulgaria. The preliminary results show a good agreement across all the models in representing scenarios projecting historical trends, while a major discrepancy is detectable when the rate of implementation of renewable energy is forced to increase to achieve energy system decarbonization. The discrepancy is mainly traceable to the differences in the models’ conception and structures rather than in a real mismatch in representing the same scenarios. The present study is put forward as a guideline for validating new modeling approaches that link energy policy decision tools to the global biophysical and socioeconomic constraints.

Development of a hybrid multi-criteria decision-making approach for sustainability evaluation of bioenergy production technologies: A case study

Abstract Many countries that derive their energy from fossil fuel sources are turning to renewable, and environmentally friendly energy sources to alleviate the growing concerns about global warming and environmental issues. Bioenergy technology is one of the potential alternatives for renewable energy systems. This paper evaluates sustainable bioenergy production technologies through a case study in Egypt. A comprehensive methodology has been developed in which experts and decision makers are able to use linguistic terms to express their opinions and participate in the decision making required to prioritize the dimensions that affect the sustainability of bioenergy production technologies. Identification of the optimum bioenergy production technology and setting its priorities is a difficult task; many dimensions must be taken into account in the evaluation process, such as the environmental, technical, economic, and social dimensions and their sub-indicators. This paper therefore applies a hybrid multi-criteria decision-making (MCDM) approach that takes into account many conflicting dimensions. In addition, handling of uncertainty was conducted under a neutrosophic environment using trapezoidal neutrosophic numbers (TNNs). Initially, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was employed to identify the relative importance of the dimensions and their sub-indicators. Then, Evaluation based on Distance from Average Solution (EDAS) was employed to rank alternatives. An illustrative case considering seven bioenergy production technologies was studied to confirm the feasibility of the suggested approach, and comparisons were performed to show the advantages of the hybrid MCDM techniques. Sensitivity analysis is conducted to prove the validity and stability of the developed framework with variations in weights. The results of this work provide useful information for energy policy decision makers and the results of the case study indicate that the conversion of agricultural and municipal wastes to biogas is the most suitable sustainable bioenergy technology with a weight of 0.996 followed by oil crops to biodiesel technology with weight 0.539.

„Climate Impacts“ of Fossil Fuels in Today’s Energy Systems

The main fossil fuels oil, coal, and gas provide ~80% of global energy. In the next few years, “renewables” may meet only half the additional growth in global electricity demand or only about 20% of primary energy demand growth. The remaining increased energy demand will need to be matched by increased conventional energy generation. Coal and gas together account for 50% of primary energy and 60% of electricity. Therefore, the authors examine the global warming potential for both fuels. The surprising conclusion is that surfaced-mined coal appears “better for the climate” than the average natural gas and all coal appears beneficial over LNG. Therefore, current CO2-only reduction policies and CO2 taxes are leading to misguided consequences and the switch from coal to gas, especially LNG, will not have the desired impact of reducing predicted future global warming, quite the contrary. With IPCC’s global warming potential of CH4 over 20 years, a ~2% higher loss of methane across the value chain prior to combustion of gas vs. coal will lead to “climate parity” of coal with gas. Analyzing IEA information, natural gas value chains have high methane and undocumented CO2 losses. On global average, natural gas emits ~20% more CO2eq than surface-mined coal. This difference increases as the use of shale gas and LNG expands. Global GHG policies need to include documented changes in measured airborne CO2eq to avoid spending large amounts of public funds on ineffective or sub-optimal policies. I.e., most global funds, international banks, conglomerates, and insurances have publicly announced that, mostly for climate protection reasons, they will abandon coal but will continue to support LNG and pipeline natural gas projects which are all required to power human life. This is likely to have unexpected consequences. The potential effects of a warming climate system should not be discounted or played down. However, the undisputed benefits of increased CO2 concentrations in the atmosphere because of its photosynthetic and growth effects (fertilization) on plants need to be considered in energy policy decisions as well. The authors suggest sponsoring ultra-super-critical power plants, whether fossil fuel fired, nuclear, or renewable, at no cost to developing nations to reduce their emissions while also providing essential and reliable energy to encourage industrial growth. Such “technological” gifts could replace less efficient existing plants or even provide new power plants that have far higher emission reduction factors thereby supporting economic and environmental activities in both developing and developed nations. NOTE: This is a draft paper, currently for review

ANALYSIS OF THE RELATIONSHIP BETWEEN RENEWABLE ENERGY AND ECONOMIC GROWTH IN SELECTED DEVELOPING COUNTRIES

Whether there is a relationship between economic growth and energy consumption and the direction of this relationship is of great importance in energy policy decision making in countries where the state plays an active role in energy markets. If there is a relationship from energy to growth, conservative policies such as energy taxes, energy-saving, and energy prices will hurt growth. There are many studies in the literature investigating the effects of renewable and non-renewable energy consumption on the economic growth of countries. Since different data, periods, and methods were used in the studies, consensus could not be achieved. Therefore, this issue remains current and continues to be investigated. The main purpose of this study is to determine whether renewable energy consumption has a positive effect on economic growth for selected developing countries (Brazil, India, Indonesia, China, Chile, Mexico, South Africa and Turkey). Fossil energy consumption and CO 2 emissions variables are also included in the model established for this purpose. GMM estimator, one of the dynamic panel data methods, was used for empirical analysis of the relationship between these variables. Keywords: renewable energy consumption, carbon emission, economic growth, Dynamic panel data, GMM method JEL Classifications: O40, Q43, Q40 DOI: https://doi.org/10.32479/ijeep.10397

Energy and Climate Policy—An Evaluation of Global Climate Change Expenditure 2011–2018

Concern for climate change is one of the drivers of new, transitional energy policies oriented towards economic growth and energy security, along with reduced greenhouse gas (GHG) emissions and preservation of biodiversity. Since 2010, the Climate Policy Initiative (CPI) has been publishing annual Global Landscape of Climate Finance reports. According to these reports, US$3660 billion has been spent on global climate change projects over the period 2011–2018. Fifty-five percent of this expenditure has gone to wind and solar energy. According to world energy reports, the contribution of wind and solar to world energy consumption has increased from 0.5% to 3% over this period. Meanwhile, coal, oil, and gas continue to supply 85% of the world’s energy consumption, with hydroelectricity and nuclear providing most of the remainder. With this in mind, we consider the potential engineering challenges and environmental and socioeconomic impacts of the main energy sources (old and new). We find that the literature raises many concerns about the engineering feasibility as well as environmental impacts of wind and solar. However, none of the current or proposed energy sources is a “panacea”. Rather, each technology has pros and cons, and policy-makers should be aware of the cons as well as the pros when making energy policy decisions. We urge policy-makers to identify which priorities are most important to them, and which priorities they are prepared to compromise on.

A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making

Urban planners, local authorities, and energy policymakers often develop strategic sustainable energy plans for the urban building stock in order to minimize overall energy consumption and emissions. Planning at such scales could be informed by building stock modeling using existing building data and Geographic Information System-based mapping. However, implementing these processes involves several issues, namely, data availability, data inconsistency, data scalability, data integration, geocoding, and data privacy. This research addresses the aforementioned information challenges by proposing a generalized integrated methodology that implements bottom-up, data-driven, and spatial modeling approaches for multi-scale Geographic Information System mapping of building energy modeling. This study uses the Irish building stock to map building energy performance at multiple scales. The generalized data-driven methodology uses approximately 650,000 Irish Energy Performance Certificates buildings data to predict more than 2 million buildings’ energy performance. In this case, the approach delivers a prediction accuracy of 88% using deep learning algorithms. These prediction results are then used for spatial modeling at multiple scales from the individual building level to a national level. Furthermore, these maps are coupled with available spatial resources (social, economic, or environmental data) for energy planning, analysis, and support decision-making. The modeling results identify clusters of buildings that have a significant potential for energy savings within any specific region. Geographic Information System-based modeling aids stakeholders in identifying priority areas for implementing energy efficiency measures. Furthermore, the stakeholders could target local communities for retrofit campaigns, which would enhance the implementation of sustainable energy policy decisions.

Bioenergy versus forest conservation: a partial equilibrium analysis of the Swedish forest raw materials market

ABSTRACT This paper presents an economic assessment of two different policies – both implying an increased demand for forest ecosystem services – and how these could affect the competition for forest raw materials. A forest sector trade model is updated to a new base year (2016), and then employed to analyze the consequences of a more intense use of bioenergy and increased forest conservation in Sweden. These scenarios are assessed individually and in combination. A particularly interesting market impact is that bioenergy promotion and forest conservation tend to have opposite effects on forest industry by-product prices. Moreover, combining the two policies mitigates the forest industry by-product price increase compared to the case where only the bioenergy-promoting policy is implemented. Namely, the energy using sector (heat and power) is less negatively affected in terms of increased feedstock prices if bioenergy demand targets are accompanied by increased forest conservation. This effect is due to increasing pulpwood prices, which reduces pulp, paper and board production, and in turn mitigates the competition for the associated by-products. Overall, the paper illustrates the complexity of the forest raw material market, and the importance of considering demand and supply responses within and between sectors in energy and forest policy decision-making.