• Energy Research

Energy-economy-environment models are fundamental in developing realistic cost-effective climate policy. However, such models by necessity are simplified based on assumptions which co-determine the outcomes of scenario modelling. Major assumptions relate to demographic and economic development, technology evolution and deployment and policy decisions. The core of this analysis is to quantify how specific assumptions influence the outcomes of scenarios; not taking them together as usually in the literature but instead looking into them apiece. The TIMES modelling framework is broadly used for climate policy support and here we used the Portuguese version as an example. As the structure of TIMES modelling is similar in other countries and also for larger aggregates as the EU and the World, the method can be applied there quite directly, although outcomes will differ between countries due to differences in energy technologies and energy markets. The outcomes for the Portugal Baseline scenario using TIMES_PT show the relevance of this exercise in this sensitivity analysis on assumptions. Contrary to what might be expected, varying assumptions on the availability and price of energy resources lead to minor variations on GHG emissions in the modelling outcomes, less than 2% of the Baseline scenario emissions in 2020. The more relevant assumptions to overall uncertainty are related to socio-economic development, followed by assumptions on technology deployment. This detailed uncertainty analysis on assumptions helps to assess the robustness of modelling outcomes in the TIMES model framework, next to other aspects like model structure and validity.

SummaryOf all materials extracted from the earth's crust, the construction sector uses 50%, producing huge amounts of construction and demolition waste (CDW). In Beijing, presently 35 million metric tons per year (megatonnes/year [Mt/yr]) of CDW are generated. This amount is expected to grow significantly when the first round of mass buildings erected in the 1990s starts to be demolished. In this study, a dynamic material flow analysis (MFA) is conducted for Beijing's urban housing system, with the demand for the stock of housing floor area taken as the driver. The subsequent effects on construction and demolition flows of housing floor area and the concurrent consumption and waste streams of concrete are investigated for Beijing from 1949 and projected through 2050. The per capita floor area (PCFA) is a key factor shaping the material stock of housing. Observations in Beijing, the Netherlands, and Norway indicate that PCFA has a strong correlation with the local gross domestic product (GDP). The lifetime of dwellings is one of the most important variables influencing future CDW generation. Three scenarios, representing the current trend extension, high GDP growth, and lengthening the lifetime of dwellings, are analyzed. The simulation results show that CDW will rise, unavoidably. A higher growth rate of GDP and the consequent PCFA will worsen the situation in the distant future. Prolonging the lifetime of dwellings can postpone the arrival of the peak CDW. From a systematic view, recycling is highly recommended for long‐term sustainable CDW management.

To support product and technology choices toward a more sustainable future, diverse assessment methods are used, involving life cycle assessment (LCA). This raises the question of their predictive validity. Whereas, many studies focus on quantitative uncertainty, here the main aim is to address the complementary qualitative aspect of the LCA-related model variants. To that end, we first specify three general influential aspects: (1) future conditions, (2) needed predictivity, and (3) mechanism coverage. These have been translated into a more concrete checklist for qualitative predictive validity. Second, we categorized the model variants into a limited number of basic model types, based on five predefined modeling characteristics. These model types show increasingly complex steps for investigating the future, illustrated with energy systems for transport. Different answers to the same questions may result. With increasing model complexity, the relevant questions may change from analysing specific products, to more general product systems, and next to product-technology domain systems. As a third step, the qualitative predictive validity of the nine modeling types is evaluated using the developed checklist. All have limited predictive validity, increasingly so for longer time horizons, as they lack most causal mechanisms, especially the institutional drivers for development and employment of technologies to emerge. Also, the future is only partially determined. For supporting choices, the conclusion is that the comparative analysis regarding long-term also broader product-technology systems has limited predictive validity. As a solution, conditional statements may show directions for explorative analysis resulting in highly tentative advice on potentially attractive directions.

Biorefinery, an example of a multiple products system, integrates biomass conversion processes and equipment to produce fuels, power and chemicals from biomass. This study focuses on technical design, economic and environmental analysis of a lignocellulosic feedstock (LCF) biorefinery producing ethanol, succinic acid, acetic acid and electricity. As the potential worldwide demand of succinic acid and its derivatives can reach 30 million tons per year, succinic acid is a promising high-value product if production cost and market price are substantially lowered. The results of the economic analysis show that the designed refinery has great potentials compared to the single-output ethanol plant; even when the price of succinic acid is lowered or the capital investment doubled. In terms of eco-efficiency, the LCF biorefinery shows better environmental performances mainly in global warming potential due to CO(2) fixation during acid fermentation. The overall evaluation of the eco-efficiency depends on the importance attached to each impact category.

The issue of municipal solid waste (MSW) management has been highlighted in China due to the continually increasing MSW volumes being generated and the limited capacity of waste treatment facilities. This article presents a quantitative eco-efficiency (E/E) analysis on MSW management in terms of greenhouse gas (GHG) mitigation. A methodology for E/E analysis has been proposed, with an emphasis on the consistent integration of life cycle assessment (LCA) and life cycle costing (LCC). The environmental and economic impacts derived from LCA and LCC have been normalized and defined as a quantitative E/E indicator. The proposed method was applied in a case study of Tianjin, China. The study assessed the current MSW management system, as well as a set of alternative scenarios, to investigate trade-offs between economy and GHG emissions mitigation. Additionally, contribution analysis was conducted on both LCA and LCC to identify key issues driving environmental and economic impacts. The results show that the current Tianjin's MSW management system emits the highest GHG and costs the least, whereas the situation reverses in the integrated scenario. The key issues identified by the contribution analysis show no linear relationship between the global warming impact and the cost impact in MSW management system. The landfill gas utilization scenario is indicated as a potential optimum scenario by the proposed E/E analysis, given the characteristics of MSW, technology levels, and chosen methodologies. The E/E analysis provides an attractive direction towards sustainable waste management, though some questions with respect to uncertainty need to be discussed further.