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Uncertainty analysis is useful in determining whether the results of life cycle assessment are sufficiently reliable and valid when making optimal decisions. However, only a few studies have measured carbon emissions by considering the inherent uncertainty during building construction phase that may result in the misinterpretation of critical parameters. To address such weakness, a multi-method-based uncertainty analysis framework was developed in view of the basic characteristics of the construction practice. This framework integrated the deterministic and probabilistic approaches to facilitate the uncertainty assessment in quantifying carbon emissions and to provide insights into the sensitive construction activities from the uncertainty perspective. The developed framework was examined through a mix-use project in Guangzhou China. Results showed that the uncertainties in the measurement method and geographic representativeness are the major uncertainty sources for the building construction phase. The total greenhouse gas emission for the target building was 8791.5 tonnes of carbon dioxide equivalent with a 9.8% coefficient of variation, which was in line with the result calculated by the deterministic method and with the result extrapolated based on the data collected from China. The results of the scenario analysis showed that the proportion of 1% in contribution analysis and the coefficient of variation of 18% in uncertainty analysis can be regarded as the baseline for determining the critical input parameters. This study lends a useful tool for monitoring the uncertainty of LCA studies in the construction practice. In addition, this framework can facilitate to avoid the misinterpretation of the final results during the decision-making process. Although this study focuses on Chinese construction industry, it also provides good references for measuring uncertainty of greenhouse gas emissions of construction industries around the world.

Tourism has become one of the most important sectors in many countries, significantly contributing to their economic growth and development. However, the expansion of tourism has also brought about various environmental and social challenges. The relationship between tourism, economic growth, trade openness, and the environment is diverse and complex. The objective of this paper is to investigate the relationship between the international tourism development index, GDP per capita, CO2 emissions, trade openness index as well as the energy intensity index in EU 27, over the 1995–2019 period. A composite index for international tourism was developed using the Principal Component Analysis (PCA). Panel Autoregressive distributed lag (ARDL) approach is used to reveal the long- and short-run impact of GDP per capita, CO2 emissions, trade openness index as well as the energy intensity index on the tourism development index. Panel ARDL estimates confirm some of our research hypotheses: at the level of EU countries, there is a short-run relationship between tourism and GDP per capita, but only in a few EU countries, trade openness influences tourism development index. Dumitrescu-Hurlin causality test confirms long-run feedback relationship between tourism development index and trade openness, between tourism development index and CO2 emissions, and between tourism development index and GDP and unilateral causality running from tourism development index towards energy efficiency.

Cost-effective CO2 capture and storage (CCS) is critical for the rapid global decarbonization effort recommended by climate science. The increase in levelized cost of electricity (LCOE) of plants with CCS is primarily associated to the large energy penalty involved in CO2 capture. This study therefore evaluates three high-efficiency CCS concepts based on integrated gasification combined cycles (IGCC): (1) gas switching combustion (GSC), (2) GSC with added natural gas firing (GSC-AF) to increase the turbine inlet temperature, and (3) oxygen production pre-combustion (OPPC) that replaces the air separation unit (ASU) with more efficient gas switching oxygen production (GSOP) reactors. Relative to a supercritical pulverized coal benchmark, these options returned CO2 avoidance costs of 37.8, 22.4 and 37.5 €/ton (including CO2 transport and storage), respectively. Thus, despite the higher fuel cost and emissions associated with added natural gas firing, the GSC-AF configuration emerged as the most promising solution. This advantage is maintained even at CO2 prices of 100 €/ton, after which hydrogen firing can be used to avoid further CO2 cost escalations. The GSC-AF case also shows lower sensitivity to uncertain economic parameters such as discount rate and capacity factor, outperforms other clean energy benchmarks, offers flexibility benefits for balancing wind and solar power, and can achieve significant further performance gains from the use of more advanced gas turbine technology. Based on all these insights, the GSC-AF configuration is identified as a promising solution for further development.

Socio-technical or strategic approach to renewable energy deployment all suggests that the uptake of renewable energy technology such as solar photovoltaic is as much a social issue as a technical issue. Among social issues, one most direct and immediate component is the cost of the renewable energy technology. Because renewable electricity provides no new functionality—a clean electron does the same work as a dirty electron does—but is relatively expensive compared with fossil fuel based electricity, there is currently an under-supply of renewable electricity. Policy instruments based on economics approaches are therefore developed to encourage the production and consumption of renewable electricity, aiming to remediate the market inefficiencies that stem from the failure in internalizing the environmental or social costs of fossil fuels. In this vein, the most discussed instruments are renewable portfolio standard or quota based system and the general category of feed-in tariff. Feed-in tariff is to support output or generation of the renewable electricity by subsidizing revenues. The existing discussions have all concerned about the relative effectiveness of these two instruments in terms of cost, prices and implementation efficiency. This paper attempts a different basis of evaluation of these two instruments in terms of cost and (network) externality effects. The cost effect is driven by deploying the renewable as a manufactured technology, and the network externality effect is driven by deploying the renewable as an information technology. The deployment instruments are studied in terms of how these two effects are leveraged in the deployment process. Our formulation lends itself to evolutionary policy interpretation. Future research directions associated with this new energy policy framework is then suggested.

(1) Background: The present development of transport networks focusses on the better management of fuels and energy and the preservation of the environment. To fulfill these desiderates, some countries have already reconsidered the deployment plans of new highways. This research studies the feasibility of less polluting, quasi-self-powered, intelligent highway infrastructure functional blocks accommodating functions for the future introduction of smart wireless sensor grids and connected autonomous vehicles. Subject of investigation are the possibilities of energy harvesting, and the intelligent management of resources. (2) Methods: the research investigates the main technologies for energy harvesting and recommends an optimal solution. It also proposes a framework for the intelligent, AI-based management of energy and the use of an optimized backup solution relying on 5G beamforming for energy supply of the local wireless sensing network devices; (3) Results: recommendations are made for the best energy harvesting solution, an architecture of the energy management system, an algorithm for energy management and backup solution based on 5G beamforming; (4) Conclusions: the research emphasizes the advantages and drawbacks for different solutions regarding energy harvesting in an intelligent green highway scenario with a focus on the infrastructure developed to accommodate future connected and autonomous vehicles. The term “intelligent highway” must be understood in the automotive industry to describe a network of roads where cars communicate with the infrastructure and among themselves for the purpose of avoiding congestion and performing the seamless operation of services, and a space where cars and infrastructure cooperatively process information for obtaining better road safety, less pollution, and efficient energy management. With the recent recession of conventional fuel availability and the increase in prices, a solution to improving autonomy of both cars and infrastructure might be welcomed.

To increase the spatial resolution of Soil Moisture Active Passive (SMAP), this study modifies the downscaling factor model based on the Temperature Vegetation Drought Index (TVDI) using data from the Project for On-Board Autonomy (PROBA-V). In the modified model, TVDI parameters were derived from the temperature-vegetation space and the Enhanced Vegetation Index (EVI). This study was conducted in the north China region using SMAP, PROBA-V, and Moderate Resolution Imaging Spectroradiometer satellite images. The 9-km spatial resolution SMAP data was downscaled to 0.3-km spatial resolution soil moisture using a modified downscaling method. Downscaling accuracies from the original and modified downscaling factor models were compared based on field observations. The results show that both methods generated similar spatial distributions in which soil moisture estimates increased as vegetation coverage increased from built-up areas to forest. However, based on the root mean square error between observations and estimations, the modified model demonstrated an increased estimation accuracy of 4.2% for soil moisture compared to the original method. This study also implies that downscaled soil moisture shows promise as a data source for subsequent watershed scale studies.

It is presented an optimization model based on genetic algorithms for the operation of a multipurpose hydroelectric power development consisting in a pumped storage plant (PSP) with weekly operation cycle. The lower reservoir of the PSP is supplied upstream from a peak hydropower plant (HPP) with a large reservoir and supplies the own HPP which provides the required discharges towards downstream. Under these conditions, the optimum operation of the assembly consisting in 3 reservoirs and hydropower plants becomes a difficult problem if there are considered the restrictions as regards: the gradients allowed for the reservoirs filling/emptying, compliance with of a long-term policy of the upper reservoir from the hydroelectric development and of the weekly cycle for the PSP upper reservoir, correspondence between the power output/consumption in the weekly load schedule, turning to account of the water resource at maximum overall efficiencies, etc. Maximization of the net energy value (generated minus consumed) was selected as performance function of the model, considering the differentiated price of the electric energy over the week (working or weekend days, peak, half-peak or base hours). The analysis time step was required to be of 3 hours, resulting a weekly horizon of 56 steps and 168 decision variables, respectively, for the 3 HPPs of the system. These were allowed to be the flows turbined at the HPP and the number of working hydrounits at PSP, on each time step. The numerical application has considered the guiding data of Fantanele-Tarnita-Lapustesti hydroelectric development. Results of various simulations carried out proved the qualities of the proposed optimization model, which will allow its use within a decisional support program for such a development.

The overall purpose of the research is to develop a method to compute ecological flows in line with the EU Water Framework Directive (Directive 2000/60/EC) for the whole Romanian territory, for a variety of hydrological, morphological and ecological conditions. The method has three components: a Quantity component, a Dynamic component, and a Real-time operation component. The Quantity component is a hydrological method with elements of the aquatic fauna habitat indirectly linked to biological organisms based on the current Romanian knowledge on the linkages between hydrology and aquatic biology. The Dynamic and Real-time operation components are related to the hydrological forecast. The method is practical, robust and easy to apply. The concept and the ideas use the hydrological forecast to ensure the water dynamics required by the Water Framework Directive, and to develop the quantitative component, keeping in mind that putting it into practice might have importance for a broader audience. In order to better highlight the concept, the paper shows three practical examples of the RoEflow method’s application.