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Abstract For 118 million residential housing units in the U.S., there is currently a gap between the potential energy savings that can be achieved through the use of existing energy efficiency technologies, and the actual level of energy savings realized, particularly for the 37% of housing units that are considered residential rental properties. Additional quantifiable benefits are needed beyond energy savings to help further motivate residential property owners to invest in energy efficiency upgrades. This research focuses on assessing the adoption of energy efficient upgrades in U.S. residential housing and the impact on rental prices. Ten U.S. cities are chosen for analysis; these cities vary in size across multiple climate zones, and represent a diverse set of housing market conditions. Data was collected for over 159,000 rental property listings, their characteristics, and their energy efficiency measures listed in rental housing postings across each city. Following an extensive data quality control process, over thirty different types energy efficient features were identified. The level of adoption was determined for each city, ranging from 5.3% to 21.6%. Efficient lighting and appliances were among the most common, with many features doubling as energy efficient and other desirable aesthetic or comfort improvements. Then using propensity score matching and conditional mean comparison methods, the relative impact on rent charged in each city was calculated, which ranged from a 6% to 14.1% increase in rent for properties with energy efficient features, demonstrating a positive economic impact of these features, particularly for property owners. This was further subdivided into five types of energy efficiency upgrade and three housing types. Single family homes generally demanded higher premiums with energy efficient features, however there was not a consistent pattern across the types of efficient upgrades. The results of this work demonstrate that investment in energy efficient technologies has quantifiable benefits for rental property owners in the U.S. beyond just energy savings. This methodology and results can also be used in other cities and by property owners, utility companies, or others, ultimately encouraging further investment and positive economic impact in residential energy efficiency and in turn improving energy and resource conservation in the building sector.

Abstract Using a proprietary database of online job postings from 2010 to 2019, we find that job vacancies in the U.S. energy sector increasingly require high levels of “soft” skills (such as social, cognitive, people management, project management, and customer service skill), showing an “upskilling” pattern in the past decade. We further examine skill requirements across and within four major professional occupations in the U.S. energy sector and find substantial variations. Meanwhile, in the energy sector, although cognitive and social skills are the most frequently required skills, they do not positively contribute to firm productivity. Although the requirement for “hard” skills (such as products and marketing, engineering, and general computer skill) stays relatively flat, “hard” skills actually matter most in the energy sector, especially products and marketing and general computer skills are two most valuable skills, contributing the highest to energy firms. Our results indicate that energy firms should pay more attention to “hard” skills in human resource management, while not following the increasing trend of “soft” skills in hiring.

This study examines the relationship between coal consumption and economic growth for 15 emerging market economies within a multivariate panel framework over the period 1980–2006. The heterogeneous panel cointegration results indicate there is a long-run equilibrium relationship between real GDP, coal consumption, real gross fixed capital formation, and the labor force. While in the long-run both real gross fixed capital formation and the labor force have a significant positive impact on real GDP, coal consumption has a significant negative impact. The panel causality tests show bidirectional causality between coal consumption and economic growth in both the short- and long-run.

Renewable hydrogen is increasingly recognized as one of the key decarbonisation options compatible with the EU's climate neutrality goal. We quantify possible cost reductions for renewable hydrogen production until 2050 through electrolysis with off-grid renewable electricity generation systems. We focus on the use of solar PV and on- and offshore wind energy in 30 European countries. We project that towards 2050 hydrogen production costs can fall below 2 €/kg in several countries in Europe. Hybrid configurations, consisting of both onshore wind and solar PV electricity generation, generally result in lower renewable hydrogen production costs. Systems with a relatively high level of full load hours benefit from a reduced share of investment costs for the electrolyser component. The levelized cost of hydrogen produced via solar PV systems can only compete with wind-based systems when significant electrolyser cost reductions are realized, despite the ultimately low expected levelized costs of solar PV-based electricity generation. The novelty of this analysis is that it proffers an overview of the dependencies of the costs of green hydrogen production, and how these costs could decrease over the forthcoming decades across a large set of European countries. Specifically, we show how the dynamics behind the projected renewable hydrogen production costs per country highlight the role that technological learning could have in identifying the most suitable locations for hydrogen production.

The publication gives an overview of the production costs of synthetic methane in a Power-to-Gas process. The production costs depend in particularly on the electricity price and the full load hours of the plant sub-systems electrolysis and methanation. The full-load hours of electrolysis are given by the electricity supply concept. In order to increase the full-load hours of methanation, the size of the intermediate hydrogen storage tank and the size of the methanation are optimised on the basis of the availability of hydrogen. The calculation of the production costs for synthetic methane are done with economics for 2030 and 2050 and the expenditures are calculated for one year of operation. The sources of volume of purchased electricity are the short-term market, long-term contracts, direct-coupled renewable energy sources or seasonal use of surpluses. Gas sales are either traded on the short-term market or guaranteed by long-term contracts. The calculations show, that an intermediate storage tank for hydrogen, adjustment of the methanation size and operating electrolysis and methanation separately, increase the workload of the sub-system methanation. The gas production costs can be significantly reduced. With the future expected development of capital expenditures, operational expenditure, electricity prices, gas costs and efficiencies, an economic production of synthetic natural gas for the years 2030, especially for 2050, is feasible. The results show that Power-to-Gas is an option for long-term, large-scale seasonal storage of renewable energy. Especially the cases with high operating hours for the sub-system methanation and low electricity prices show gas production costs below the expected market prices for synthetic gas and biogas.

Abstract We evaluate potential global impacts of increase in U.S. natural gas exports as a result of the shale gas boom. To our knowledge this is the first such analysis using a global economic model to understand this timely policy issue. Our primary conclusion is that world economic activity is higher through most of the simulation period [2014–2035] when U.S. natural gas exports rise. The overall U.S. results mirror the global ones, but the magnitude of income gains depends upon how the rate of increase and level of exports are determined, and the price elasticity of natural gas supply. The U.S. benefits more when export increases and levels depend on natural gas production rather than when they are pre-determined by assumption. The economic impacts on other natural gas importers and exporters can change as well based on how export levels are determined. The effects on natural gas prices, consumption, and production in individual countries vary with the scenarios and model parameter values.

Abstract The share of wind energy in the US energy supply has been steadily increasing in the last two decades. With new wind energy farms being installed in various states of the country, local and multi-regional economic disruptions are bound to take place. The multi-regional economic impacts of installing new wind farms was determined using the US multi-region input-output (US-MRIO) model that has been developed, also called the USLab. Currently, there is a lack of multi-regional impact assessment of wind energy expansion in the US. In this article, we use the US-MRIO to determine regional and sectoral spill-over effects resulted from installation of wind energy farms in 10 US states. The economic impacts were calculated by feeding the USLab with data obtained from the Jobs and Economic Development Impacts (JEDI) Wind model published by National Renewable Energy Laboratory (NREL). The JEDI wind model provides the change in local economic data such as the number of new jobs created and increase of energy-related products in each region in the final demand and value-added. The data about final demand and value-added change was used with the US-MRIO model to account for the multi-regional economic impact across US due to installation of wind energy farms. The year of wind farm installation was set to 2017 and a US-MRIO for 2017 was generated to calculate the economic impact. The total economic impact was found to be 26 billion dollars of which 3 billion dollars was associated with the states where no new wind energy capacity was installed. Installation of new energy production capacity also results in “change in energy consumption” across US. Using the US-MRIO model and the energy intensity of manufacturing sectors, the energy consumption increase due to addition of wind farms was found to be about 6952 trillion of btu for the total change in economic throughput. Primary metal manufacturing and Machinery manufacturing sectors stood out amongst other manufacturing sectors with considerable change in energy consumption with an increase of 3074 trillion of btu and 1537 trillions of btu.

Abstract Carbon dioxide emissions trading systems (ETS) are an important market-based mitigation strategy and have been applied in many regions. This study evaluates the potential for a national ETS in China. Using a dynamic computable general equilibrium (CGE) model with detailed representations of economic activity, emissions, and income distribution, we examine alternative mitigation policies from now until 2050. Based on statistical and survey data, we disaggregate the labor and household sectors and simulate the impacts of ETS policies on the incomes of different household groups. We find that ETS has the potential to reconcile China’s goals for sustained, inclusive, and low-carbon economic growth. Results show some key findings. First, the number of unemployed people in energy-intensive industries such as coal and construction will continue to increase; by 2050, employment in the coal industry will decline by 75%. Second, if the scope of the carbon market extends to all industries in China, carbon market revenues will continue to increase, reaching a maximum of 2278 billion yuan ($336 billion) in 2042 to become the world's largest carbon market. Third, the distribution of benefits from the national ETS can help achieve greater social equity. By comparing different distribution policies, we find that the combination of targeted subsidies for unemployed coal workers and direct household subsidies based on proportional per capita will reduce the social income gap to the greatest extent compared with other scenarios. By 2050, this distribution policy will reduce the Gini coefficient in China by 10% compared to the Business as Usual (BAU) scenario.