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The paper explores the potential of the main state economic incentive (Conto Termico) designed to promote the use of renewable energy sources for heating and cooling purposes in residential buildings, aiming to reduce primary air emissions from residential biomass combustion (RBC). For this purpose, an emission reduction scenario involving the technological transition of biomass residential heating appliances was implemented in the lowland small‐medium municipalities of the Lombardy region in Italy, where households are connected to the natural gas grid but RBC is often practiced for economic reasons, contributing to severe air pollution problems related especially to particulate matter (PM) emissions. This technological turnover scenario (i.e. all biomass appliances in the area replaced by state‐of‐the‐art pellet appliances) resulted in a significant reduction of particulate matter emissions, while having only a minimal impact on NOx emissions. The incentivized cost for technological turnover (€15/kgPM10 avoided) was in line with the social costs associated with health impacts related to PM10 in a rural setting such as the study area. Given the one‐off investment support provided by a recently adopted national decree (DM 15/09/2022), biomethane emerges as a promising renewable energy alternative to decrease PM and NOx emissions from RBC in Lombardy region, characterized by an extensive natural gas distribution network and potential waste feedstock for biomethane production. Despite the still elevated production costs, an analysis restricted to the incentivized segment reveals that supporting biomethane production through investment incentives is more cost‐effective (€8/kgPM10 avoided) than promoting technological turnover. [ABSTRACT FROM AUTHOR]

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Although numerous studies in the literature have been conducted to model CO2 emissions, there is a lack of empirical knowledge of consumption‐based CO2 emissions, which are adjusted for international trade, specifically. Therefore, the present study aims to close this gap in the literature in the case of Italy, while capturing the asymmetric effect of trade, renewable energy, and economic growth on consumption‐based CO2 emissions. The present study uses the Gregory–Hansen test for cointegration with regime shifts, Markov switching regression, nonlinear autoregressive distributed lag (NARDL), and frequency domain causality test. The study's outcomes reveal that (1) the asymmetric effect of import on consumption‐based CO2 emissions is positive, implying that rising import is associated with declining consumption‐based environmental quality; (2) export, renewable consumption, and economic growth reduce consumption‐based CO2 emissions in Italy. Moreover, these outcomes are supported by the outcomes of the frequency domain causality test. These innovative insights may prompt policy‐makers to implement eco‐friendly methods, such as renewable energy distribution and environmental innovation, to achieve a greener future. Integr Environ Assess Manag 2022;18:784–795. © 2021 SETAC Key Points: Rising import is associated with declining the consumption‐based environmental quality.Export, renewable consumption, and economic growth reduce consumption‐based CO2 emissions. [ABSTRACT FROM AUTHOR]

The need to investigate suitable alternatives to conventional fossil fuels has increased interest in several renewable energy sources, especially in widely available sources of biomass. This has made environmental and socio‐economic improvements possible. In recent years, biogas from biomass has increasingly been considered the most feasible alternative to energy from fossil fuel, as it allows both the possibility to reduce waste disposal treatment and the opportunity to produce green energy. Opuntia ficus‐indica (OFI) has been suggested as a possible biomass as it could represent a suitable resource for producing biomethane as a new frontier within the context of a circular economy. This study aims to define a methodology for evaluating the distribution of OFI biomass that is potentially available for biogas production by applying maximum entropy modeling in a case study of a province located in southern Italy. The geographic information system (GIS)‐based model that was developed made it possible to estimate the suitability of the species in the territory and to define specific indices at the municipal level by enabling the localization of the highest productive territorial areas. Municipalities were grouped into three different classes ranging from low to high potential ones. In future years, new municipalities could be part of the high‐potential group, due to global warming resulting from climate change. The results could be relevant to the intervention priorities established by the European Union related to the planning activities supported by the European Structural and Investment Funds within the Smart Specialization Strategy. In this regard, knowledge of the potential production and distribution of a species in a territory is highly valuable information for the quantification of the biomass potentially available for biogas production, prior to strategic planning for the improvement of the biogas sector. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd [ABSTRACT FROM AUTHOR]

Abstract: In liberalized markets, there usually exists a day‐ahead session where energy is sold and acquired for the following production day. Owing to the high uncertainty of its production, renewable energy (wind in particular) can significantly influence the network imbalance of the following day. In this work, we consider the problem of predicting the sum of the bid volumes for wind energy of all the producers inside the day‐ahead energy market. This is a valuable tool to be used by an energy provider in order to determine the imbalance of a market zone and, thus, properly size its bids. In particular, we focus on the estimation of the possible relationship between the meteorological forecasts and the wind power offered on the market by the companies for a market zone. We propose a machine learning model which is used to compute a 1‐day‐ahead forecast. The input‐output mapping is obtained by support vector regression. The input feature vector is defined by a suitable feature extraction technique since the meteorological forecasts are given on a lattice of thousands of geographical points. The computational experiments are performed considering the Italian market as a case study (years 2012‐2016). The results show that the proposed feature extraction technique, selecting only some geographical zones, manages to reduce the error attained using all the features. Moreover, classical statistical methods are shown to be outperformed by machine learning models. The analysis reveals also some weaknesses of the model, which may be due to other nonmeteorological factors at play. [ABSTRACT FROM AUTHOR]