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This paper introduces a computationally efficient method for estimating structural parameters of dynamic discrete choice models with large choice sets. The method is based on Poisson pseudo maximum likelihood (PPML) regression, which is widely used in the international trade and migration literature to estimate the gravity equation. Unlike most of the existing methods in the literature, it does not require strong parametric assumptions on agents' expectations, thus it can accommodate macroeconomic and policy shocks. The regression requires count data as opposed to choice probabilities; therefore it can handle sparse decision transition matrices caused by small sample sizes. As an example application, the paper estimates sectoral worker mobility in the United States.

Discrete Choice Models --- Gravity Equation --- International Economics & Trade --- Labor Mobility --- Migration --- Poisson Pseudo Maximum Likelihood

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Municipal solid waste management continues to be a major challenge for local governments in both urban and rural areas across the world, and one of the key issues is their financial constraints. Recently an economic analysis was conducted in Eryuan, a poor county located in Yunnan Province of China, where willingness to pay for an improved solid waste collection and treatment service was estimated and compared with the project cost. This study finds that the mean willingness to pay is about 1 percent of household income and the total willingness to pay can basically cover the total cost of the project. The analysis also shows that the poorest households in Eryuan are not only willing to pay more than the rich households in terms of income percentage in general, but also are willing to pay no less than the rich in absolute terms where no solid waste services are available; the poorest households have stronger demand for public solid waste management services while the rich have the capability to take private measures when public services are not available.

Contingent valuation method --- Economic analysis --- Energy --- Energy and Environment --- Environment --- Environment and Energy Efficiency --- Environmental Economics & Policies --- Multiple bounded discrete choice --- Municipal solid waste management --- Small towns --- Urban Solid Waste Management --- Waste Disposal & Utilization

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Municipal solid waste management continues to be a major challenge for local governments in both urban and rural areas across the world, and one of the key issues is their financial constraints. Recently an economic analysis was conducted in Eryuan, a poor county located in Yunnan Province of China, where willingness to pay for an improved solid waste collection and treatment service was estimated and compared with the project cost. This study finds that the mean willingness to pay is about 1 percent of household income and the total willingness to pay can basically cover the total cost of the project. The analysis also shows that the poorest households in Eryuan are not only willing to pay more than the rich households in terms of income percentage in general, but also are willing to pay no less than the rich in absolute terms where no solid waste services are available; the poorest households have stronger demand for public solid waste management services while the rich have the capability to take private measures when public services are not available.

Contingent valuation method --- Economic analysis --- Energy --- Energy and Environment --- Environment --- Environment and Energy Efficiency --- Environmental Economics & Policies --- Multiple bounded discrete choice --- Municipal solid waste management --- Small towns --- Urban Solid Waste Management --- Waste Disposal & Utilization

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With the Internet of Things and various information and communication technologies, a city can manage its assets in a smarter way, constituting the urban development vision of a smart city. This facilitates a more efficient use of physical infrastructure and encourages citizen participation. Smart energy and smart mobility are among the key aspects of the smart city, in which the electric vehicle (EV) is believed to take a key role. EVs are powered by various energy sources or the electricity grid. With proper scheduling, a large fleet of EVs can be charged from charging stations and parking infrastructures. Although the battery capacity of a single EV is small, an aggregation of EVs can perform as a significant power source or load, constituting a vehicle-to-grid (V2G) system. Besides acquiring energy from the grid, in V2G, EVs can also support the grid by providing various demand response and auxiliary services. Thanks to this, we can reduce our reliance on fossil fuels and utilize the renewable energy more effectively. This Special Issue “Smart Energy and Intelligent Transportation Systems” addresses existing knowledge gaps and advances smart energy and mobility. It consists of five peer-reviewed papers that cover a range of subjects and applications related to smart energy and transportation.

Technology: general issues --- History of engineering & technology --- electric vehicles --- PROSA --- PROMETHEE for Sustainability Assessment --- MCDA --- Multi-Criteria Decision Analysis --- stochastic analysis --- Monte Carlo --- uncertainty --- cargo bicycles --- loading hub --- facility location problem --- computer simulations --- Python programing --- electric vehicle charging --- vehicle-to-grid --- genetic algorithms --- particle swarm optimization --- demand-side management --- discrete choice theory --- revenue management --- road-railway accidents --- classification trees --- road safety --- transport means --- accidents victims --- condition monitoring --- vibroacoustic diagnostics --- gearbox --- power transmission systems --- neural networks --- deep learning --- electric vehicles --- PROSA --- PROMETHEE for Sustainability Assessment --- MCDA --- Multi-Criteria Decision Analysis --- stochastic analysis --- Monte Carlo --- uncertainty --- cargo bicycles --- loading hub --- facility location problem --- computer simulations --- Python programing --- electric vehicle charging --- vehicle-to-grid --- genetic algorithms --- particle swarm optimization --- demand-side management --- discrete choice theory --- revenue management --- road-railway accidents --- classification trees --- road safety --- transport means --- accidents victims --- condition monitoring --- vibroacoustic diagnostics --- gearbox --- power transmission systems --- neural networks --- deep learning

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With the Internet of Things and various information and communication technologies, a city can manage its assets in a smarter way, constituting the urban development vision of a smart city. This facilitates a more efficient use of physical infrastructure and encourages citizen participation. Smart energy and smart mobility are among the key aspects of the smart city, in which the electric vehicle (EV) is believed to take a key role. EVs are powered by various energy sources or the electricity grid. With proper scheduling, a large fleet of EVs can be charged from charging stations and parking infrastructures. Although the battery capacity of a single EV is small, an aggregation of EVs can perform as a significant power source or load, constituting a vehicle-to-grid (V2G) system. Besides acquiring energy from the grid, in V2G, EVs can also support the grid by providing various demand response and auxiliary services. Thanks to this, we can reduce our reliance on fossil fuels and utilize the renewable energy more effectively. This Special Issue “Smart Energy and Intelligent Transportation Systems” addresses existing knowledge gaps and advances smart energy and mobility. It consists of five peer-reviewed papers that cover a range of subjects and applications related to smart energy and transportation.

electric vehicles --- PROSA --- PROMETHEE for Sustainability Assessment --- MCDA --- Multi-Criteria Decision Analysis --- stochastic analysis --- Monte Carlo --- uncertainty --- cargo bicycles --- loading hub --- facility location problem --- computer simulations --- Python programing --- electric vehicle charging --- vehicle-to-grid --- genetic algorithms --- particle swarm optimization --- demand-side management --- discrete choice theory --- revenue management --- road–railway accidents --- classification trees --- road safety --- transport means --- accidents victims --- condition monitoring --- vibroacoustic diagnostics --- gearbox --- power transmission systems --- neural networks --- deep learning --- n/a --- road-railway accidents