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PurposeThis study aims to explore the relationship among energy consumption, real income, financial development and oil prices in Italy over the period 1960-2014.Design/methodology/approachDifferent econometric techniques – such as the General Methods of Moment (GMM) or the AutoRegressive Distributed Lags (ARDL) bounds test – are usually used in the empirical analysis. Moreover, both the Toda and Yamamoto causality tests and the Granger causality tests are applied to the data.FindingsThe results of unit root and stationarity tests show that the variables are non-stationary at levels, but stationary in first-differences form, or I(1). The ARDL boundsF-test reveals an evidence of a long-run relationship among the four variables at 1% significance level. Moreover, an increase in real GDP and oil prices has a significant effect on energy consumption in the long run. The coefficients of estimated error correction term are also negative and statistically significant. In addition, the paper explores the causal relationship between the variables by using a VAR framework, with Toda and Yamamoto but also Granger causality tests, within both multivariate and bivariate systems. The findings indicate that energy consumption is affected by real GDP.Originality/valueThe study also filled the literature gap of applying ARDL technique to examine this relevant issue for Italy.

Photovoltaic (PV) modules and solar plants are one of the main drivers towards zero-carbon future. Energy communities that are engaging citizens through collective energy actions can reinforce positive social norms and support the energy transition. Furthermore, by incorporating Artificial Intelligence (AI) techniques, innovative applications can be developed with huge potential, such as supply and demand management, energy efficiency actions, grid operations and maintenance actions. In this context, the scope of this paper is to present an approach for forecasting an energy cooperative’s solar plant short term production by using its infrastructure and monitoring system. More specifically, four Machine Learning (ML) and Deep Learning (DL) algorithms are proposed and trained in an operational solar plant producing high accuracy short-term forecasts up to 6 hours. The results can be used for scheduling supply of the energy communities and set the base for more complex applications that require accurate short-term predictions, such as predictive maintenance.

This paper presents details of the practical implementation of a coordinated reactive power-voltage controller (CQVC) developed for a multi-machine steam power plant. The CQVC performs optimal coordination of the synchronous generators' reactive power outputs in order to maintain the total reactive power delivered by a steam power plant (SPP) or the voltage at a steam power plant HV busbar. This is required to achieve slow preventive control of generated reactive powers in order to maximize reactive power reserves along with slow high side voltage control. The CQVC is designed to act through existing automatic voltage regulator inputs, thus no modification of an existing excitation system was made. In order to increase the robustness to changes in operating conditions and to achieve the desired shape of the reactive power response, the two-step predictor corrector method is implemented. A newly developed algorithm for steady state detection ensures non-interference of reactive power voltage control with excitation system response. The paper also presents practical design solutions to overcome limitations posed by application to a real system. Acceptance and functional tests carried out during the commissioning of the CQVC are also discussed in this paper.

Sustainable development has been a concept, a movement, and a commitment, one of a kind. In its short lifetime, it has been subject to many changes and improvements. Many countries have joined under the umbrella of sustainable development and its goals, but the definition of sustainable development has remained ambiguous. In this paper, the ambiguity of this definition with a postmodern look has been studied to deconstruct it with the hope of finding the roots of skepticism about this concept. Here it has been argued that instead of ignoring the skepticism, we must acknowledge its causes and try to eliminate them. Nevertheless, the result of deconstruction is not a definitive answer it shows us the path to do this by making sure that any invention we hope might come under this vision was compatible with current rules and procedures at the same time. (The article is now accepted for publication in the journal: World Review of Science, Technology and Sustainable Development. Before publication, due to journal policy, the prior-to-peer-review manuscripts can not be publicly available.)

(Uploaded by Plazi for the Bat Literature Project) Amazonian forests play a key role in the global carbon cycle, but there is much uncertainty about the quantity and distribution of carbon stored in these forests. We quantified total aboveground dry biomass (TAGB) in undisturbed central Amazonian rainforests, based on detailed estimates of all live and dead plant material within 20 1 ha plots spanning an extensive (ca. 1000 km2) study area. TAGB values in our study area were very high, averaging 397:7 Æ 30:0 Mg haÀ1. The most important component of aboveground biomass was large (!10 cm diameter-at-breast-height (DBH)) trees, which comprised 81.9% of TAGB, followed by downed wood debris (7.0%), small trees, saplings, and seedlings (<10 cm DBH; 5.3%), lianas (2.1%), litter (1.9%), snags (1.5%), and stemless palms (0.3%). Among large trees, aboveground biomass was greatest in intermediate-sized (20–50 cm DBH) stems (46.7% of TAGB), with very large (!60 cm DBH) trees also containing substantial biomass (13.4% of TAGB). There were no significant correlations between large tree biomass and that of any other live or dead biomass component. An analysis based on the variability of our samples suggested that just 3–4 randomly positioned 1 ha plots would be sufficient to provide a reasonable estimate of mean TAGB in a landscape such as ours (with 95% confidence intervals being <10% of the mean). This suggests that efforts to quantify Amazon forest biomass should be extensive rather than intensive; researchers should sample many geographically separate areas with a few plots each, rather than sampling a small number of areas more intensively.

This paper describes newly designed coordinated Q-V controller (CQVC), for a multi machine steam power plant (SPP). After giving design specification and theoretical background for designed CQVC, the paper focuses on practical implementation and performance of commissioned CQVC. Following several innovations are introduced in design of controller: sensitivity matrix concept is applied at plant level to decouple generators connected to the same busbars, steady state detection is used for the decoupling between synchronous generator dynamics and network dynamics, the response is shaped by the use of predictor corrector method. Issues related to practical implementation of CQVC in SPP such as online estimation of network reactance and generators' reactive power capability are also addressed and discussed in the paper. Finally the paper shows examples of recorded plant responses after commissioning of the proposed controller.

This study aims to investigate the nexus among waste generation, economic growth, and greenhouse gas (GHG) emissions in a circular economy framework for the case of Switzerland. Using two different empirical approaches (Dynamic Auto-Regressive Distributed Lags and Fuzzy Cognitive Maps), time-series results show that municipal waste and economic growth have both a short- and a long-run impact on GHG emissions. Moreover, causality analyses evidence the presence of a unidirectional causal flow running from municipal waste and economic growth to greenhouse gas emissions, while a bidirectional causality between municipal waste and economic growth. The results of the static analysis of the municipal solid waste cognitive map show that the most significant system variables relate to the domains of “policy drivers” (education and awareness campaigns and extended producer responsibility) and “environment and health” (GHG emissions). Findings of the policy scenario simulations reveal that the most effective drivers are those about the mission-oriented policy approach. https://www.sciencedirect.com/science/article/abs/pii/S095965262201174X?dgcid=coauthor

The consumers preference towards the usage of Green products helps us determine how willing a consumer is to contribute towards making the planet a better place to live in. The contribution may be through purchase and usage of organic products and thus adding to the overall sustainability of the environment. Consumers have different purchasing preferences and these preferences are constantly varying as are sult of the availability of other alternatives to choose from. This research provides a valuable suggestion about the interests of green product consumers in the city of Bengaluru, by measuring factors like willingness to pay. The increasing number of consumers who prefer and are willing to buy these products are subjected to the buying process. Green business must understand the requirements of the ecofriendly consumers to survive in the market.