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Avoiding irreversible climate change as effectively as possible is one of the most pressing challenges of society. Carbon pricing that is uniformly valid on a global and cross-sectoral basis represents a cost-efficient policy tool to meet this challenge. Carbon pricing allows external costs to be allocated or internalized on a polluter-pays principle. It is shown that a global emissions cap-and-trade system is the most suitable market-based instrument for reducing global emissions levels, in line with the temperature goal set by the Paris Agreement. A proposal for its design is presented in this paper. This instrument encourages worldwide measures, with the lowest marginal abatement cost, according to a pre-defined reduction path. Thereby, it ensures compliance with a specified remaining carbon budget to meet a certain temperature limit in a cost-efficient manner. Possible reduction paths are presented in this paper. Weaknesses in the design of existing emissions trading systems (ETS), such as the EU ETS, are identified and avoided in the proposed instrument. The framework solves several problems of today’s climate change policies, like the free rider problem, carbon leakage, rebound effects or the green paradox. The introduction of a global uniform carbon pricing instrument and its concrete design should be the subject of policy, especially at the United Nations climate change conferences, as soon as possible in order to allow for rapid implementation. If a global ETS with a uniform carbon price could be introduced, additional governmental regulations with regard to carbon emissions would become obsolete.

Direct Numerical Simulations (DNS) have been conducted to study the response of initially laminar spherical premixed methane–air flame kernels to successively higher turbulence intensities at five different equivalence ratios. The numerical experiments include a 16-species/25-step skeletal mechanism for methane oxidation and a multicomponent molecular transport model. Highly turbulent conditions (with integral Reynolds numbers up to 4513) have been accessed. The effect of turbulence on the physical properties of the flame, in particular its consumption speed Sc, which is an interesting measure of the turbulent flame speed ST has been investigated. Local quenching events are increasingly observed for highly turbulent conditions, particularly for lean mixtures. The obtained results qualitatively confirm the expected trend regarding correlations between u′/SL and the consumption speed: Sc first increases, roughly linearly, with u′/SL (low turbulence zone), then levels off (bending zone) before decreasing again (quenching limit) for too intense turbulence. For a fixed value of u′/SL, Sc/SL varies with the mixture equivalence ratio, showing that additional parameters should probably enter phenomenological expressions relating these two quantities.

The biogas production technology has improved over the last years for the aim of reducing the costs of the process, increasing the biogas yields, and minimizing the greenhouse gas emissions. To obtain a stable and efficient biogas production, there are several design considerations and operational parameters to be taken into account. Besides, adapting the process to unanticipated conditions can be achieved by adequate monitoring of various operational parameters. This paper reviews the research that has been conducted over the last years. This review paper summarizes the developments in biogas design and operation, while highlighting the main factors that affect the efficiency of the anaerobic digestion process. The study’s outcomes revealed that the optimum operational values of the main parameters may vary from one biogas plant to another. Additionally, the negative conditions that should be avoided while operating a biogas plant were identified.

Background. Laws that enable a circular economy (CE) are being enacted globally, but reliable standardized and digitized CE data about products is scarce, and many CE platforms have differing exclusive formats. In response to these challenges, the Ministry of The Economy of Luxembourg launched the Circularity Dataset Standardization Initiative to develop a globalized open-source industry standard to allow the exchange of standardized data throughout the supply cycle, based on these objectives: (a) Provide basic product circularity data about products. (b) Improve circularity data sharing efficiency. (c) Encourage improved product circularity performance. A policy objective was to have the International Organization for Standardization (ISO) voted to create a working group. Methods. A state-of-play analysis was performed concurrently with consultations with industry, auditors, data experts, and data aggregation platforms. Results. Problem statements were generated. Based on those, a solution called Product Circularity Data Sheet (PCDS) was formulated. A proof of concept (POC) template and guidance were developed and piloted with manufacturers and platforms, thus fulfilling objective (a). For objective (b), IT ecosystem requirements were developed, and aspects are being piloted in third party aggregation platforms. Objective (c) awaits implementation of the IT ecosystem. The policy objective related to the ISO was met. Conclusions and future research. In order to fully test the PCDS, it is necessary to: conduct more pilots, define governance, and establish auditing and authentication procedures.

The capacity factor of a power plant is the ratio of generation over its potential generation. It is an important measure to describe wind and solar resources. However, the fluctuating nature of renewable power generation makes it difficult to integrate all generation at times. Whenever generation exceeds the load, curtailment or storage of energy is required. With increasing renewable shares in the power system, the level of curtailment will further increase. In this work, the influence of the curtailment on the capacity factors for a highly renewable German power system is studied. An effective capacity factor is introduced, and the implications for the distribution of renewable power plants are discussed. Three years of highly-resolved weather data were used to model wind and solar power generation. Together with historical load data and a transmission model, a possible future German power system was simulated. It is shown that effective capacity factors for unlimited transmission are strongly reduced by up to 60% (wind) and 70% (photovoltaics) and therefore of limited value in a highly renewable power system. Furthermore, the results demonstrate that wind power benefits more strongly from a reinforced transmission grid than photovoltaics (PV) does.

Photovoltaic (PV) technology has been the focus of interest due to its nonpolluting operation and good installation flexibility. Irradiation and temperature are the two main factors which impact the performance of the PV system. Accordingly, when partial shading from surroundings occurs, its incident shadow diminishes the irradiation and reduces the generated power. Since the conventional maximum power point tracking methods (MPPT) could not distinguish the global maximum power of the power-voltage (P-V) characteristic curve, a new tracking method needs to be developed. This paper proposes a global maximum power point tracking method using shading detection and the trend of slopes from each section of the curve. Full mathematical equations and algorithms are presented. Simulations based on real weather data were performed both in short-term and long-term studies. Moreover, this paper also presents the experiment using the DC-DC synchronous and interleaved boost converter. Results from the simulation show an accurate tracking result and the system can enhance the total energy generated by 8.55% compared to the conventional scanning method. Moreover, the experiment also confirms the success of the proposed tracking algorithm.

In the international movement to combat the threat of climate change, the timely implementation of residential energy-saving practises is becoming an urgent issue. Because the number of apartments is increasing, we analysed data from home energy management systems (HEMSs) and data from questionnaire surveys of 309 households in a condominium. We focused on the seasonal variation in air-conditioning (AC) use in living-dining rooms to determine the tendency of energy use for heating/cooling related to the characteristics of flats, the profiles of residents, and energy-saving behaviours. In winter, 80% of residents mainly used gas floor heating rather than AC and 24% did not use AC in winter. In households where someone stays home for long hours, they prefer gas floor heating rather than AC in winter. These households also tend to engage in energy-saving behaviours to adjust the indoor thermal environment. There are several types of energy-saving lifestyles; therefore, effective energy-saving measures should be considered for both energy efficiency and the thermal comfort of residents.

Accurate and timely macro forecasting requires new and powerful predictors. Carbon emissions data with high trading frequency and short releasing lag could play such a role under the framework of mixed data sampling regression techniques. This paper explores the China case in this regard. We find that our multiple autoregressive distributed lag model with mixed data sampling method setup outperforms either the auto-regressive or autoregressive distributed lag benchmark in both in-sample and out-of-sample nowcasting for not only the monthly changes of the purchasing managers’ index in China but also the Chinese quarterly GDP growth. Moreover, it is demonstrated that such capability operates better in nowcasting than h-step ahead forecasting, and remains prominent even after we account for commonly-used macroeconomic predictive factors. The underlying mechanism lies in the critical connection between the demand for carbon emission in excess of the expected quota and the production expansion decision of manufacturers.