Clusters de geração de energia renovável para o setor sucroenergético brasileiro

Review of baseline studies on energy policies and indicators in Malaysia for future sustainable energy development

This study examines the dynamic effects of electricity generation (EG) on CO2 emissions from the residential sector. The study focuses on the EU-4 countries (Germany, Spain, France, and Italy), considers residential CO2 emissions as the dependent variable, and includes disaggregated level fossil and renewable EG as explanatory variables. In this context, the study runs nonlinear quantile-on-quantile (QQ) regression and Granger causality in quantiles (GQ) as the main models with daily data from January 2, 2019, to March 10, 2023, while quantile regression (QR) is used for robustness check. The findings present that in terms of CO2 emissions: (i) EG from coal, natural gas, and oil has a stimulating effect at higher quantiles in all countries; (ii) EG from hydro has an increasing effect at higher quantiles, while it has a decreasing effect at lower and middle quantiles in all countries except France; (iii) EG from solar has a dampening effect at higher quantiles in all countries except France; (iv) EG from wind has a declining effect at higher quantiles in both Spain and France; (v) both fossil and renewable energy EG have a causal effect on residential sector CO2 emissions at the disaggregated level except at some quantiles. Overall, the effect size and the causal effect of EG on CO2 emissions change for quantiles, countries, and EG sources. Therefore, the study proposes to rely on the specific EG sources for Germany and Italy (solar energy), Spain, and France (wind energy) to mitigate climate change by reducing residential CO2 emissions.

The greatest sustainability challenge facing humanity today is the greenhouse gas emissions and the global climate change with fossil fuels led by coal, natural gas and oil contributing 61.3% of global electricity generation in the year 2020. The cumulative effect of the Stockholm, Rio, and Johannesburg conferences identified sustainable energy development (SED) as a very important factor in the sustainable global development. This study reviews energy transition strategies and proposes a roadmap for sustainable energy transition for sustainable electricity generation and supply in line with commitments of the Paris Agreement aimed at reducing greenhouse gas emissions and limiting the rise in global average temperature to 1.5°C above the preindustrial level. The sustainable transition strategies typically consist of three major technological changes namely, energy savings on the demand side, generation efficiency at production level and fossil fuel substitution by various renewable energy sources and low carbon nuclear. For the transition remain technically and economically feasible and beneficial, policy initiatives are necessary to steer the global electricity transition towards a sustainable energy and electricity system. Large-scale renewable energy adoption should include measures to improve efficiency of existing nonrenewable sources which still have an important cost reduction and stabilization role. A resilient grid with advanced energy storage for storage and absorption of variable renewables should also be part of the transition strategies. From this study, it was noted that whereas sustainable development has social, economic, and environmental pillars, energy sustainability is best analysed by five-dimensional approach consisting of environmental, economic, social, technical, and institutional/political sustainability to determine resource sustainability. The energy transition requires new technology for maximum use of the abundant but intermittent renewable sources a sustainable mix with limited nonrenewable sources optimized to minimize cost and environmental impact but maintained quality, stability, and flexibility of an electricity supply system. Technologies needed for the transition are those that use conventional mitigation, negative emissions technologies which capture and sequester carbon emissions and finally technologies which alter the global atmospheric radiative energy budget to stabilize and reduce global average temperature. A sustainable electricity system needs facilitating technology, policy, strategies and infrastructure like smart grids, and models with an appropriate mix of both renewable and low carbon energy sources.

Assessment of renewable electricity generation by pumped storage power plants in EU Member States

Under the pressure of climate-related issues, importance of clean electricity generation (EG) has been developing in ensuring sustainable environment. Accordingly, countries have been trying to increase clean EG. Although increasing clean EG is important, it is also critical that clean EG alternatives should not cause displacement. Otherwise, there may not be certain progress in stimulating clean EG. So, this study aims to investigate displacement between nuclear and renewable EG types. In this context, the study considers four clean EG sources (i.e. nuclear, hydro, solar, and wind), analyzes G7 countries, uses high-frequency daily data between 1 January 2019 to 30 December 2023, applies novel Wavelet Local Multiple Correlation (WLMC) method as the main model, and performs Wavelet Transform Coherence (WTC) for the robustness check. The study defines that (i) hydro and wind EG have a displacement (supporting) effect on nuclear EG at short (long) term; (ii) solar EG has a supporting (displacement) effect on nuclear EG at short (long) term; (iii) the most critical EG source is nuclear for France and United States and hydro for Great Britain and Japan; (iv) the WTC method reveals the robustness. The study determines that renewable EG has a displacement (supporting) effect on nuclear EG in short (long) term. Therefore, there are certain conflict between nuclear and renewable EG in the short term, whereas they support each other in the long term. Thus, there is a need for comprehensive and balanced energy transition policy so that choices should not cause a displacement among alternatives.

Due to increasing geopolitical tensions and disruption of gas supplies, European countries have been looking for alternatives for electricity generation (EG). As part of this process, one of the most important goals for long-term sustainability is to ensure carbon neutrality. Therefore, this study analyzes time, frequency, and quantile-based impacts of EG from different electricity generation sources (i.e., renewable, nuclear, and fossil fuels) on carbon neutrality, focusing on four leading European countries. The study applies the wavelet transform coherence (WC), quantile-on-quantile regression (QQ), and Granger causality in quantiles (GQ) to high-frequency daily data between January 2, 2019 and March 10, 2023. Results show that (i) there is a strong time and frequency dependence between EG and CO2 emissions across countries, while results vary by EG sources and countries; (ii) renewable EG dampens CO2 emissions. At higher quantiles, a higher share of renewable EG lowers CO2 emissions in Germany and Spain, while they increase in France; (iii) nuclear EG is beneficial only for the United Kingdom. (iv) Fossil EG increases CO2 emissions in all countries. Excessive fossil EG leads to more CO2 emissions at higher quantiles; (v) the impacts of EG on CO2 emissions have a time-, frequency-, quantile-, country-, and EG source-dependent structure. The outcomes of the study demonstrates that the ideal EG source for countries is mainly renewable EG, while in the case of the United Kingdom, nuclear EG could be an alternative for improving the environment while reducing fossil fuels.

This study examines the role of renewable electricity generation (EG) on environmental quality in the top 10 carbon dioxide (CO2) emitting European countries by considering ongoing energy crisis. By doing so, the research considers the environmental effect of renewable EG (such as hydro, solar, and wind) at a disaggregated level from January 2, 2019 to August 31, 2023, using the wavelet coherence (WC), quantile-on-quantile (QQ) regression and Granger causality in quantiles (GQ) methods. The outcomes demonstrate that (i) hydro, solar, and wind EG have a strong dependence on CO2 emissions, while effects vary across times and frequencies; (ii) hydro EG reduces CO2 emissions in Germany, Belgium, and Austria; (iii) at higher quantiles, solar EG curbs CO2 emissions in Italy, Spain, Romania, and Austria; (iv) at higher quantiles, wind EG lowers CO2 emissions in all countries except Bulgaria; (v) hydro, solar, and wind EG generally have a causal effect on CO2 emissions across all quantiles except for some lower (0.05–0.10), middle (0.40–0.60), and higher (0.90–0.95) quantiles. The results thus show that CO2 emissions in European countries are strongly influenced by renewable EG as a function of time, frequency, and quantile. In line with the outcomes, the study suggests continuing to rely on hydro EG for Germany, solar EG for Italy and Austria, and wind EG for all other countries as the best EG alternative for achieving a carbon-neutral economy. In this way, countries can benefit from renewable EG and at the same time contribute to their energy security.

The current examination aims to explore the critical relationship of energy, in the form of electricity with economic growth of Indonesia. Contrary to traditional approach of assessing the impact of energy consumption, the present study analyzes the association from production point of view by assessing the impact of electricity production on economic development. In doing so, the current study has adopted the refined methodology of Auto-Regressive Distributed Lags (ARDL) bound testing approach to examine the dynamic relationship among renewable electricity generation, non-renewable electricity generation and economic growth with amplified understanding of the critical association to support the course of economic planning and policy making. The results of ARDL bound testing approach confirm that renewable electricity generation, non-renewable electricity generation and carbon dioxide emission are solid determinants of economic development in Indonesia. Moreover, the results avow that renewable electricity and non-renewable electricity generation have a useful and beneficial outcome on economic development in Indonesia

The quest to eradicate poverty, central to the United Nations Sustainable Development Goals (SDGs), poses a significant global challenge. Advancement in sustainable rural development is critical to this effort, requiring the seamless integration of environmental, economic, and governmental elements. Previous research often omits the complex interactions among these factors. Addressing this gap, this study evaluates sustainable rural development in China by examining the interconnection between agricultural production and government-led poverty reduction, with annual rainfall considered an influential factor of climate change impacts on these sectors and overall sustainability. Utilizing a Meta-frontier entropy network dynamic Directional Distance Function (DDF) within an exogenous Data Envelopment Analysis (DEA) model, we categorize China’s 27 provinces into southern and northern regions according to the Qinling-Huaihe line for a comparative study of environmental, economic, and governmental efficiency. This innovative approach overcomes the limitations of previous static analyses. The findings reveal: (1) Rainfall, as an exogenous variable, significantly affects agricultural production efficiency. (2) The overall efficiency in both southern and northern regions increases when accounting for rainfall. (3) Government effectiveness in poverty reduction is comparatively lower in the northern region than in the southern region when rainfall is considered. These insights underscore the importance of including climatic variables in sustainable development policies and emphasize the need for region-specific strategies to bolster resilience against climatic challenges.

Electricity use is growing worldwide, providing a range of energy services: lighting, heating and cooling, specific industrial uses, entertainment, information technologies, and mobility. Because its generation remains largely based on fossil fuels, electricity is also the largest and the fastest-growing source of energy-related CO2 emissions, the primary cause of human-induced climate change. Forecasts from the IEA and others show that “decarbonising” electricity and enhancing end-use efficiency can make major contributions to the fight against climate change. Global and regional trends on electricity supply and demand indicate the magnitude of the decarbonisation challenge ahead. As climate concerns become an essential component of energy policy-making, the generation and use of electricity will be subject to increasingly strong policy actions by governments to reduce their associated CO2 emissions. Despite these actions, and despite very rapid growth in renewable energy generation, significant technology and policy challenges remain if this unprecedented essential transition is to be achieved. The IEA Climate and Electricity Annual 2011 provides an authoritative resource on progress to date in this area, with statistics related to CO2 and the electricity sector across ten regions of the world. It also presents topical analyses on meeting the challenge of rapidly curbing CO2 emissions from electricity, from both a policy and technology perspective.

In routine breast cancer clinical practice, multiple serial sections are stained for guiding therapy. These include the most widely used hematoxylin & eosin (H&E) as well as established immunohistochemical biomarkers such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and increasingly so, Ki67, collectively known as the IHC4. Analysis of these sections involves visual assessment which is labor intensive and inefficient. More importantly, it ignores the wealth of spatial information in histological samples that can be used to better understand clonal heterogeneity, which may potentially be responsible for mixed response to treatment and drug resistance. Material and Methods: We have developed an automated image registration algorithm that aligns H&E and IHC4 serial section images from the same tumor by employing a combination of rigid and non-rigid registration. This automated image registration algorithm subsequently enables us to develop SpEeCH (spatial expression of clonal heterogeneity), a novel method to identify spatial clusters with differential expression. At the core of SpEeCH is a clustering method that exploits spatial cellular characteristics of tumor regions to identify spatial clonal clusters. Gaussian mixture Model (GMM) is used in combination with the Bayesian information criterion and resampling to identify the optimal number of stable clusters. As GMM ignores local spatial dependency which is an important tumor characteristic, we are also comparing GMM with conditional random fields that are undirected graphical models to account for local dependencies instead of full joint distribution. To discover spatial clusters, we applied SpEeCH to a set of 13 high-grade surgically resected breast tumors with serial sections of H&E and IHC4. Tumor regions were defined as 5 μm x 5 μm squares, resulting in >10,000 regions for clustering in each tumor. Clinical implications of the spatial clusters were verified in an independent dataset (METABRIC) that consists of gene expression whole-tumor profiles and disease-specific survival data of 2,000 breast cancer tumors. Results: By employing SpEeCH, we identified five stable spatial clusters based on histological section of H&E and IHC4 in the discovery set, with significantly different disease-specific survival validated in METABRIC gene expression data (p=0, log-rank test). This analysis revealed a group of poor prognosis ER-/HER2+/Ki67+ patients (p=0, hazard ratio = 4.4, 95% confidence interval = 3.0-6.3, log-rank test compared to a good prognosis group ER+/HER2-/Ki67-). Marked differences in prognosis among these groups highlight the significance of identified spatial clusters. For example, one of the tumors in the discovery set contains both ER-/HER2+/Ki67+ and ER+/HER2-/Ki67- clones. Therefore, such clonal heterogeneity, which is ignored during diagnosis, may help explain treatment resistance. To this end, our observation confirmed the clinical relevance of spatial clusters by leveraging a large-scale dataset under the assumption that such data represents signal from major clones. We propose that the number of clonal clusters and their spatial relationship within a tumor can be used as measures of intra-tumor heterogeneity. Their efficacy is currently being evaluated on independent clinical datasets. Conclusion: Large numbers of histology samples are available from clinical diagnostics and research laboratories, yet there is a lack of tools that facilitate objective analysis of tumor heterogeneity in these samples. Compared to high-cost multimarker immunofluorescence approaches, our automated image and statistical analysis pipeline enables efficient and effective analysis of available histology samples, which is critical for understanding cancers as highly heterogeneous diseases. Citation Format: Andreas Heindl, Adnan Mujahid Khan, Yinyin Yuan. SpEeCH: Quantifying Spatial Expression of Clonal Heterogeneity in breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-51.

This study aims to analyse the dynamic relationship between renewable electricity generation and its determinants in Malaysia from 1980 to 2016. F-Bound test and VECM are applied. A dynamic long-run relationship exists among the variables use. Long-run elasticity of labour and non-renewable electricity generation on renewable electricity generation is positive elastic and negative elastic, respectively. The short-run elasticity of capital, GDP and financial development on renewable electricity generation is negative elastic, positive elastic and negative inelastic respectively. Also, long-run bidirectional causality between FD and renewable electricity generation, unidirectional causality running from capital, labour, non-renewable electricity generation, and GDP to renewable electricity generation is discovered. However, short-run unidirectional causality from capital and labour to renewable electricity generation and renewable electricity generation to FD is found. Accordingly, these findings highlight important messages to policymakers in the process of sustainable energy through the determinants influence the renewable electricity generation in Malaysia.

Do solar photovoltaic clean development mechanism projects contribute to sustainable development in Latin America? Prospects for the Paris Agreement

In the context of increasing sustainability through renewable energy utilization in Saudi Arabia, this study was motivated by the need to understand the impact of COVID-19 on gym-goers’ attitudes and behaviors towards renewable energy generating using their human kinetic energy (HKE). A comparative analytical study was conducted using a pre-COVID-19 survey (n = 96) and a post-COVID-19 survey (n = 385) of gym-goers in Saudi Arabia. The surveys consisted of closed-ended Likert-type questions that measured participants’ attitudes, behaviors, and willingness related to renewable energy and HKE generation. The results of the Chi-square test of independence showed that there was no significant difference in the participants’ attitudes towards behaviors and awareness regarding renewable energy and HKE generation between the pre-COVID-19 and post-COVID-19 surveys. However, the study revealed an overall promising level of support for sustainable energy and HKE generation among gym-goers. This led to the conclusion that there is potential for the installation of sustainable HKE generation systems in gyms. The main implication of this finding is the need for greater education and awareness raising campaigns as well as incentives to support the implementation of renewable energy solutions. The research contributes to sustainable development and Saudi Vision 2030 by exploring potential opportunities for increasing the share of renewable energy in the overall energy mix and promoting sustainable development.

Artificial intelligence-driven green innovation for sustainable development: Empirical insights from India's renewable energy transition.