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Abstract Kenya has made considerable policy efforts to expand its renewable energy portfolio to meet energy demand and mitigate greenhouse gas (GHG) emissions. Development of proper policies requires a robust framework for analyzing the benefits of renewable energy investments. Towards this end, this study applied a choice experiment analysis to determine how attributes (type of energy, ownership, impact on environment, distance and visibility, community job creation, and yearly renewable energy tax) impact the public willingness to pay for renewable energy development in Kenya. A nationwide survey of 1020 households was conducted in nine counties using conditional logit (MNL) and random parameter logit (RPL) frameworks. The results reveal that the Kenyan public places a high value on environmental impact, followed by type of renewable energy and community job creation, respectively. On the other hand, respondents do not place much emphasis on ownership or distance and visibility. Policy simulation suggests that while renewable energy adoption is highly valued by households, the total willingness to pay is not enough to cover the higher capital cost for the development of various renewable energy technologies.

Integrated assessment models (IAMs) are playing an increasingly important role in long-run sustainability analysis. At their core is a set of global economic and environmental accounts which capture a complete set of inter-industry and inter-regional relationships in the global economy in a consistent manner. While much attention is focused on the raw data and parameterization required to expand or add sectoral detail to IAMs, only rarely is there discussion of how different matrix balancing methods (i.e. translating disparate raw data sources into the consistent database) affect modeling results. This article offers an in-depth look into the database–modeling nexus in IAMs, focusing on the electric power sector which is both a major source of CO2 emissions and a critical vehicle for climate change mitigation. Comparisons of the prevailing matrix balancing algorithms show how the choice of database reconciliation methodology affects modeling results using policy-relevant simulations in the context of the electric power sector. The resulting insights can be applied to the disaggregation of other, technology rich sectors in the economy. We conclude that appropriate selection of database reconciliation methodologies can reduce the deviation between bottom-up and top-down modeling.

The paper proposes a critical assessment of municipal solid waste gasification today, starting from basic aspects of the process (process types and steps, operating and performance parameters) and arriving to a comparative analysis of the reactors (fixed bed, fluidized bed, entrained bed, vertical shaft, moving grate furnace, rotary kiln, plasma reactor) as well as of the possible plant configurations (heat gasifier and power gasifier) and the environmental performances of the main commercially available gasifiers for municipal solid wastes. The analysis indicates that gasification is a technically viable option for the solid waste conversion, including residual waste from separate collection of municipal solid waste. It is able to meet existing emission limits and can have a remarkable effect on reduction of landfill disposal option.

Abstract Real-world vehicle operating mode data (2.5 million 1 Hz records), collected by instrumenting the vehicles of 82 volunteer drivers with OBD datalogger and GPS while they drove their routine travel routes, were analyzed to quantify vehicle emissions estimate errors due to road grade and driving style in rural, hilly Vermont. Data were collected in winter and summer for MY 1996 and newer passenger cars and trucks only. EPA MOVES2010b was used to estimate running exhaust emissions associated with measured vehicle activity. Changes in vehicle specific power (VSP) and MOVES operating mode (OpMode) due to proper accounting for real-world road grade indicated emission rate errors between 10% and 48%, depending on pollutant, chiefly because grade-related changes in VSP could shift activity by as many as six OpModes, depending on road type. The correct MOVES OpMode assignment was made only 33–55% of the time when road grade was not included in the VSP calculation. Driving style of individual drivers was difficult to assess due to unknown traffic operations data, but the largest differences between individual drivers were observed on rural restricted roads, where traffic conditions and control have minimal impact. The results suggest the importance of (1) measuring and incorporating real-world road grade in order to correctly assign MOVES emission rates; and (2) developing a driving style typology to account for differences in the MOVES emissions estimates due to driver variability.

The Keelung port, which is located on the northern tip of Taiwan, right next to the Taipei metropolitan area, is an important international harbor. However, any air pollutants generated from the Keelung port region, immediately travel to the neighboring Keelung city, and greatly impact the residents' daily life and the quality of their environment. This study has investigated and quantified pollution emissions, from the Keelung port region, between 1997 and 2002. Emissions from major air pollution sources were estimated. The estimated results indicated that total TSP (total suspended particles) emissions had significantly increased, from 5221 ton/yr in 1997 to 262 687 ton/yr in 2002, due to the greatly increased volume of sand imported into Keelung Harbor. Quantities of other emissions, such as SO(2), NO(2), CO and HC remained stable and were 440, 207, 78 and 25 ton/yr, respectively, on average, with variations within 7% over the previous six-year period. By examining the emissions from pollution sources, it was found that TSP emissions mainly originated from re-suspension of dust, due to both vehicle movement and the sand unloading process; this accounted for over 99% of the total TSP emissions produced in the port region. About 80% of the total SO(2) emissions originated from the main ships' engines within the Keelung port region, due to the use of fuel with a high sulfur content. In addition, loading/unloading machines within the port region were the major sources of NO(2), CO and HC pollution emissions, which comprised 54, 58 and 66% of the total emissions of these pollutants, respectively. TSP emissions from Keelung port were much higher than from the neighboring Keelung city; hence, alleviating TSP emissions should be the first priority for air pollution reduction within both the port of Keelung and Keelung city.

Along with the growth and globalization of the whole world economy since the First Industrial Revolution, the production and use of fossil fuels have led to increased CO2 emissions and, ultimately, significant environmental degradation. The impact of globalization, economic growth, and renewable energy sources on CO2 may show trends with different turning points in developing countries, and estimations may need to follow Fourier-type functions to capture the frequency domain. Therefore, the aim of this study is to examine the effects of renewable energy production, economic globalization, and economic growth on CO2 emissions for Turkey in the period 1971-2006 with Fourier autoregressive distribution lag (ADL) cointegration, DOLS, and Fourier-Granger causality tests. The originality of this study is the estimation of a model of CO2 emissions with a Fourier-type function for the first time. The findings indicate a negative relationship between renewable energy production and CO2 emissions and a positive relationship between economic globalization and economic growth and CO2 emissions. In addition, according to the empirical results, there exists a one-way causality relationship between economic globalization to CO2 and economic globalization to renewable energy production, and there is evidence of a bidirectional causality relationship between economic globalization and economic growth in this study.

Robust mitigation options will play significant role in achieving the target of limiting global change to below 1.5 °C above pre-industrial levels by 2100. To support cooperation for mitigation development, we establish a real options-based model to evaluate the rational decisions of exercising the abandon option for carbon capture and storage-enhanced oil recovery (CCS-EOR) projects under oil market and geological uncertainties. Three possible cooperative mechanisms (fixed carbon dioxide (CO2) price, oil-indexed CO2 price, and joint venture contracts) among CO2-EOR stakeholders are evaluated. The results show that the conflicts in profit maximization targets for different stakeholders in cooperative mitigation are to a great extent unable to be avoided. A joint venture business model is preferred in cooperative mitigation as it can effectively weaken such conflicts. And it is more reasonable to provide incentives to the downstream of the CO2-EOR chain than compensating the adoption cost of carbon capture technologies in the upstream. From a global perspective, the inefficient cooperation can be a main barrier that hinders the development of deep-cutting options. Global mitigation strategies should not only focus on promoting technology progress but also the design of innovative business models to balance the benefits among stakeholders. A joint venture business model is recommended in both the developed and developing countries for seizing the early mitigation opportunities.

Cogeneration of heat and electricity is an important pillar of energy and climate policy. To plan the production and distribution system of combined heat and power (CHP) systems for residential heating, suitable methods for decision support are needed. For a comprehensive feasibility analysis, the integration of the location and capacity planning of the power plants, the choice of customers, and the network planning of the heating network into one optimization model are necessary. Thus, we develop an optimization model for electricity generation and heat supply. This mixed integer linear program (MILP) is based on graph theory for network flow problems. We apply the network location model for the optimization of district heating systems in the City of Osorno in Chile, which exhibits the “checkerboard layout” typically found in many South American cities. The network location model can support the strategic planning of investments in renewable energy projects because it permits the analysis of changing energy prices, calculation of break-even prices for heat and electricity, and estimation of greenhouse gas emission savings.

Renewable energy has become more popular since it is cost-effective and more efficient than conventional energy sources. Biomass-based renewable energy is primarily used in emerging economies to ensure environmental sustainability. This study examines the asymmetric correlation between biomass energy consumption and CO2 emissions in the top-10 biomass energy consumer countries (Brazil, Canada, Thailand, China, Italy, India, Germany, USA, UK, and Japan). A new approach "Quantile-onQuantile (QQ)" is employed by utilizing the data from 1991 to 2018. Biomass energy consumption, with the exception of Thailand, significantly mitigates CO2 emissions at various quantiles in selected countries. As a robustness check, we used the quantile regression test, whose findings are consistent with the outcomes from the quantile-on-quantile method. However, the degree of asymmetry in the biomass energy-CO2 nexus varies by country, necessitating extra attention and government vigilance when developing biomass energy and environmental policies.