In this paper, we forecast the price of CO2 emission allowances using an artificial intelligence tool: neural networks. We were able to provide confident predictions of several future prices by processing a set of past data. Different model structures were tested. The influence of subjective economic and political decisions on price evolution leads to complex behavior that is hard to forecast. We analyzed correlations with different economic variables related to the price of CO2 emission allowances and found the behavior of two to be similar: electricity prices and iron and steel prices. They, along with CO2 emission allowance prices, were included in the forecasting model in order to verify whether or not this improved forecasting accuracy. Only slight improvements were observed, which proved to be more significant when their respective time series trends or fluctuations were used instead of the original time series. These results show that there is some sort of link between the three variables, suggesting that the price of CO2 emission allowances is closely related to the time evolution of the price of electricity and that of iron and steel, which are very pollutant industrial sectors. This can be regarded as evidence that the CO2 market is working properly.
Cities are territories vulnerable to climate change. An alternative to increase resilience and mitigate the effects of the climate context is urban forest planning to increase ecosystem services. This research constructed a forest cover sustainability index, based on 147 semi-structured interviews with residents of four residential areas of the city of Santo Domingo (Gazcue, Zona Colonial, Ciudad Nueva, and San Carlos), in which information was collected based on both benefit perception and tree management in their home and nearby public areas. The socioeconomic characteristics of the population and the information gathered from the measurements of the urban forest in both public and private areas of the city during the 2016–2019 period were considered, including these four residential areas, which established the ecosystem services provided by the urban forest. The results showed that Gazcue had a higher value in the forest cover sustainability index. The factors that influenced this result were: job stability, medium-high income, and property ownership. Likewise, the added value of the territory, whether in terms of tourism or the socioeconomic value of the population that inhabits it, is closely related to a greater attention to urban planning, prioritizing the conservation and landscape harmony that the arboreal component can provide. In conclusion, urban forest planning in cities should consider tree species, the design and structure of spatial arrangements, and a competent legal framework that can meet the challenges of territorial sustainability and contribute to the resilience and mitigation of climate change impacts.
In this paper, we analyze the effects of the energy transition process on economic growth in Spain and Portugal, countries that, adhering to European Union (EU) directives, opted to promote clean energies from the very start. On the one hand, we look at the energy transition laws introduced by the EU and other countries. On the other, we conduct a causal analysis of energy consumption and economic growth to confirm whether the change of energy model has generated positive effects on economic growth. The procedure was as follows. First, we conducted an aggregate causality analysis exploring the relationship between growth and energy consumption. As the results were not significant, we repeated the analysis with different disaggregations of renewable energy sources. With respect to solar thermal energy and economic growth, the main conclusion is that the data appear to show a one-way causal relationship for Portugal and EU-26 (European Union without Portugal and Spain) and a two-way relationship for Spain.
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