Comparing the cost-effectiveness of drones, camera trapping and passive acoustic recorders in detecting changes in koala occupancy.

Quantifying the cost-effectiveness of alternative sampling methods is crucial for efficient biodiversity monitoring and detection of population trends. In this study, we compared the cost-effectiveness of three novel sampling methods for detecting changes in koala (Phascolarctos cinereus) occupancy: thermal drones, passive acoustic recorders and camera trapping. Specifically, we fitted single-season occupancy-detection models to data recorded from 46 sites in eight bioregions of New South Wales, Australia, between 2018 and 2022. We explored the effect of weather variables on daily detection probability for each method and, using these estimates, calculated the statistical power to detect 30%, 50% and 80% declines in koala occupancy. We calculated power for different combinations of sites (1-200) and repeat surveys (2-40) and developed a cost model that found the cheapest survey design that achieved 80% power to detect change. On average, detectability of koalas was highest with one 24-h period of acoustic surveys (0.32, 95% CI's: 0.26, 0.39) compared to a 25-ha flight of drone surveys (0.28, 95% 0.15, 0.48) or a 24-h period of camera trapping consisting of six cameras (0.019, 95% CI's: 0.014, 0.025). We found a negative quadratic relationship between detection probability and air temperature for all three methods. Our power and cost analysis suggested that 148 sites surveyed with acoustic recorders deployed for 14 days would be the cheapest method to sufficiently detect a 30% decline in occupancy with 80% power. We recommend passive acoustic recorders as the most efficient sampling method for monitoring koala occupancy compared to cameras or drones. Further comparative studies are needed to compare the relative effectiveness of these methods and others when the monitoring objective is to detect change in koala abundance over time.

Comparative life cycle assessment of integrated renewable energy-based power systems.

Integrated renewable-based power cycles should be employed to produce more sustainable electricity. This is a comparative life cycle assessment (LCA) of three combined power plants, encompassing: case 1 involving combined geothermal and wind, case 2 featuring combined geothermal and solar, and case 3 integrating wind and solar systems. The base case perovskite solar cell (PSC) modelling assumes a 3-year lifespan and a power conversion efficiency of 17 %. However, diverse scenarios are evaluated through a sensitivity assessment involving enhancements in lifetime and efficiency. The base case evaluation emphasizes that the phases with the most significant negative environmental effects which includes the drilling of geothermal wells, construction of wind plants, and manufacturing and installation of PSCs. The midpoint findings indicate that boosting the power conversion efficiency of PSC from 17 % to 35 % yields a notable decrease in environmental impact. Moreover, extending the lifetime from 3 to 15 years led to reduction in CO2 emissions from 0.0373 and 0.0185 kg CO2 eq/kWh to 0.026 and 0.0079 kg CO2 eq/kWh in cases 2 and 3, respectively. Assessing worst and best-case scenarios highlights significant declines in certain impact categories. In case 3, terrestrial ecotoxicity (TE), photochemical oxidant formation (POF), human toxicity (HT), marine ecotoxicity (ME), and marine eutrophication (MU) saw reductions exceeding 88 % compared to worst-case results. The environmental effects observed in cases 2 and 3 stem from toxicity and metal depletion, mainly linked to the PSC. Endpoint results revealed that when considering a PSC lifespan of 10 years or more, the detrimental ecosystem impacts of cases 2 and 3 become less severe than those of case 1. Uncertainty assessment has been done for different cases and impact categories. The study's results are also novel in which it evaluated the innovative PSC technology when integrated with other renewable resources, contrasting it with other integrated plants.

Characterization and risk assessment of polycyclic aromatic hydrocarbons from the emission of different power generator.

Epileptic power supply in Sub-Saharan countries of Africa has warranted the use of power generators as an alternative source of power supply. Exhaust emission from these generators is associated with Polycyclic Aromatic Hydrocarbon (PAHs). Hence, this study focused on the determination of levels of PAHs in the emission of different brands of power generators used in Nigeria. Exhaust emissions of different power generators were sampled using a filter-sorbent sampling system with polyurethane foam (PUF) as an adsorbent material. Analysis of PAHs was carried out using a Gas Chromatograph coupled to a mass selective detector (GC- MS) operated on Electron Ionization (EI) mode. The results showed the ∑ PAHs range 14.91-26.0 µ g m - 3 . Bap was the most abundant of all the compounds with a concentration of 2.6 µ g m - 3 with a range of 2.08-3.07 µ g m - 3 . The Incremental Life Cancer Risk (ILCR) values of all the generator's emission sampled are higher than 10- 4 for both children and adult which indicate a high potential cancer risk from inhalation of emission from these generators while Hazard Quotient (HQ) values from all the power generating set in this study are all above 1 which indicated high associated non-carcinogenic. The study revealed the levels of PAHs associated with the emission of power generators in Nigeria.

Gas engine CCHP system optimization: An energy, exergy, economic, and environment analysis and optimization based on developed northern goshawk optimization algorithm.

This paper aims to enhance the design and operation of a Combined Cooling, Heating, and Power (CCHP) system utilizing a gas engine as the primary energy source for a residential building in China. An Energy, Exergy, Economic, and Environment (4E) analysis is employed to assess the system's performance and impact based on energy, exergy, economic, and environmental criteria. The effectiveness of the DNGO algorithm is evaluated on a case study site and compared with Northern Goshawk Optimization (NGO) and Genetic Algorithm (GA). The findings demonstrate that the DNGO algorithm identifies the optimal gas engine size of 130 kW. The algorithm's search capabilities are greatly enhanced by this unique blend, surpassing what traditional methods can offer. The DNGO algorithm brings several advantages, including unparalleled energy efficiency, reduced exergy destruction, and a substantial decrease in C O 2 emissions. This not only supports environmental sustainability but also aligns with global standards. Economically, the algorithm enhances the performance of the CCHP system, evident through a reduced payback period and increased annual profit. Additionally, the algorithm's rapid convergence rate allows it to reach the optimal solution faster than its counterparts, making it advantageous for time-sensitive applications. Incorporating innovative methods like chaos theory, the DNGO algorithm effectively avoids local optima, enabling a broader search for the best solution. The utilization of Lévy flight further enhances the algorithm's ability to escape local optima and navigate the search space more efficiently. Additionally, swarm intelligence is employed to simulate the collective behavior of decentralized systems, aiding in problem-solving. This research represents a significant advancement in optimization techniques for CCHP systems and offers a fresh perspective to the field of swarm-based optimization algorithms.

Household-level data on well-being, inequalities, and social capital in Western Province, Zambia.

This article presents survey data from households from the Muoyo-Mukukutu area in Western Province, Zambia based on stratified sampling. Data from 411 households were collected using a questionnaire survey from 2022. Understanding the complexities of well-being is crucial for informing policies to enhance the quality of life and reduce multidimensional poverty in developing countries. Hence, the survey focuses on subjective and objective well-being and their determinants. Survey data contains details on various dimensions of objective well-being, such as living standards, health, and nutrition. It also covers the issue of subjective well-being (life satisfaction), including the related concept of freedom of choice. Moreover, we collected detailed information about diverse forms of inequalities and deprivations at the societal and intra-household level, paying particular attention to the areas of social capital and decision-making power. Additionally, the data contain details about the relationships with and attitudes to traditional leaders and statutory government representatives, respondents' economic activities and aspirations (with a special focus on agriculture), and their various socio-demographic characteristics. Individual survey results can be compared with a robust set of data as we intentionally used questions applied in other international surveys when possible.

Day-ahead resilience-economic energy management and feeder reconfiguration of a CCHP-based microgrid, considering flexibility of supply.

Many challenges have emerged due to the intense integration of renewables in the distribution system and the associated uncertainties in power generation. Consequently, local management strategies are developed at the distribution level, leading to the emergence of concepts such as microgrids. Microgrids include a variety of heating, cooling, and electrical resources and loads, and the operators' aim is to minimize operation and outage costs. Since significant distribution system outages are typically caused by events such as earthquakes, floods, and hurricanes, microgrid operators are compelled to improve resilience to ensure uninterrupted service during such conditions. A mixed-integer linear programming model is designed in this paper to optimize the energy management and structural configuration of microgrids. This optimization aims to enhance resilience cost, minimizing operation and capital costs as well as power loss and pollution. To achieve these goals, several tools are implemented including reconfiguration, storages, combined cooling, heat and power units, wind turbines, photovoltaic panels, as well as capacitors. Four case studies are defined to prove the developed model efficiency. The first case study focuses on energy management in the microgrid for operation cost minimization. The second case study emphasizes the improvement of resilience alongside energy management, aiming at minimizing costs and enhance resilience. In the third case, the microgrid's reconfiguration capability is also added to the second case. Therefore, this case aims to optimize both energy and structural management within the microgrid to simultaneously enhance resilience and minimize operational costs. Finally, in the fourth case, the problem is studied in a multi-objective approach. By comparing the results, the resilience impact on the operation of microgrids is elucidated. By considering the resilience concept in microgrid operation and based on the results of case 2, it is found that the operating costs are increased by an average of 10.38 %. However, because of reducing resilience costs by an average of 13.91 %, the total cost is reduced by an average of 5.93 % in case 2 compared to case 1. Furthermore, when comparing cases 2 and 3, the reconfiguration effect can be determined. It can be observed that the operating costs are decreased by an average of 4.5 %. Moreover, the resilience cost is decreased by an average of 1.61 %, resulting in an overall reduction of the total objective function by an average of 2.43 % in case 3 compared to case 2.

GNSS-Based Narrow-Angle UV Camera Targeting: Case Study of a Low-Cost MAD Robot.

One of the key challenges in Multi-Spectral Automatic Diagnostic (MAD) robot design is the precise targeting of narrow-angle cameras on a specific part of the equipment. The paper shows that a low-cost MAD robot, whose navigation system is based on open-source ArduRover firmware and a pair of low-cost Ublox F9P GNSS receivers, can inspect the 8 × 4 degree ultraviolet camera bounding the targeting error within 0.5 degrees. To achieve this result, we propose a new targeting procedure that can be implemented without any modifications in ArduRover firmware and outperforms more expensive solutions based on LiDAR SLAM and UWB. This paper will be interesting to the developers of robotic systems for power equipment inspection because it proposes a simple and effective solution for MAD robots' camera targeting and provides the first quantitative analysis of the GNSS reception conditions during power equipment inspection. This analysis is based on the experimental results collected during the inspection of the overhead power transmission lines and equipment inspections on the open switchgear of different power plants. Moreover, it includes not only satellite, dilution of precision, and positioning/heading estimation accuracy but also the direct measurements of angular errors that could be achieved on operating power plants using GNSS-only camera targeting.

[Assessment of carbon reduction and sink enhancement potential of photovoltaic+mining ecological restoration model]. / å ‰ä¼+矿山生态修复模式的减碳增汇潜力评估.

The energy oriented mine ecological restoration mode of photovoltaic+ecological restoration provides a breakthrough for alleviating the dilemma of photovoltaic land development and solving the urgent need for restoration of abandoned mining land. Taking a mining area in central Liaoning Province as an example, we established three photovoltaic+mining ecological restoration modes, including forest-photovoltaic complementary, agriculture-photovoltaic, and grass photovoltaic complementation. Combined with the life cycle assessment method, we calculated and assessed the potential of photovoltaic+mining ecological restoration in carbon reduction and sink enhancement. The average annual carbon reduction and sink increase was 514.93 t CO2·hm-2 under the photovoltaic+mining ecological restoration mode, while the average annual carbon reduction per megawatt photovoltaic power station was 1242.94 t CO2. The adoption of photovoltaic+ecological restoration mode in this mining area could make carbon reduction and sink enhancement 6.30-7.79 Mt CO2 during 25 years. The carbon reduction and sink increment mainly stemmed from the photovoltaic clean power generation induced carbon reduction, accounting for 96.4%-99.4%, while the contribution of ecosystem carbon sink increment was small, accounting for only 0.6%-3.7% of the total. Among different photovoltaic+ecological restoration modes, the carbon reduction and sink increment was the largest in forest-photovoltaic complementary (7.11 Mt CO2), followed by agriculture-photovoltaic (7.04 Mt CO2), and the least in grass photovoltaic complementation (6.98 Mt CO2). Constructing the development mode of "photovoltaic+mining ecological restoration" could effectively leverage the dual benefits of reducing emissions from photovoltaic power generation and increase sinks from mining ecological restoration, which would be helpful for achieving the goal of carbon neutrality in China.

Making waves: Power-to-X for the Water Resource Recovery Facilities of the future.

The wastewater industry and the energy system are undergoing significant transformations to address climate change and environmental pollution. Green hydrogen, which will be mainly obtained from renewable electricity water electrolysis (Power-to-Hydrogen, PtH), has been considered as an essential energy carrier to neutralize the fluctuations of renewable energy sources. PtH, or Power-to-X (PtX), has been allocated to multiple sectors, including industry, transport and power generation. However, considering its large potential for implementation in the wastewater sector, represented by Water Resource Recovery Facilities (WRRFs), the PtX concept has been largely overlooked in terms of planning and policymaking. This paper proposes a concept to implement PtX at WRRFs, where sourcing of water, utilization of the oxygen by-product, and PtX itself can be sustainable and diversified strategies. Potential value chains of PtX are presented and illustrated in the frame of a WWRF benchmark simulation model, highlighting the applications of oxygen from PtX through pure oxygen aeration and ozone disinfection. Opportunities and challenges are highlighted briefly, and so is the prospective outlook to the future. Ultimately, it is concluded that 'coupling PtX to WRRFs' is a promising solution, which will potentially bring sustainable opportunities for both WRRFs and the energy system. Apart from regulatory and economic challenges, the limitations in coupling PtX to WRRFs mainly come from energy efficiency concerns and the complexity of the integration of the water framework and the energy system.

Some inconvenient truths about decarbonization, the hydrogen economy, and power-to-X technologies.

The decarbonization of the energy sector has been a subject of research and of political discussions for several decades, gaining significant attention in the last years. It is commonly acknowledged that the most obvious way to achieve decarbonization is the use of renewable energy sources. Within the context of the energy sector decarbonization, many mainly industrialized countries recently started developing national plans to establish a hydrogen-based economy in the very near future. The plans for green hydrogen initially try to (a) target sectors that are difficult to decarbonize and (b) address issues related to the storage and transportation of CO2-free energy. To achieve almost complete decarbonization, electric power must be generated exclusively from renewable sources. In so-called Power-to-X (PtX) technologies, green hydrogen is generated from electricity and subsequently converted to another energy carrier which can be further stored, transported and used. In PtX, X stands, for example, for liquid hydrogen, methanol or ammonia. The challenges associated with decarbonization include those associated with (a) the expansion of renewable energies (e.g., high capital demand, political and social issues), (b) the production, transportation, and storage of hydrogen and the energy carriers denoted by X in PtX (e.g., high cost and low overall efficiency), and (c) the expected significant increase in the demand for electrical energy. The paper discusses whether and under which conditions the current national and international hydrogen plans of many industrialized countries could lead to a maximization of decarbonization in the world. It concludes that, in general, as long as the conditions for generating large excess amounts of green electricity are not met, the quick establishment of a hydrogen economy could not only be very expensive, but also counterproductive to the worldwide decarbonization efforts.

Defining equity, its determinants, and the foundations of equity science.

Advancing equity as a priority is increasingly declared in response to decades of evidence showing the association between poorer health outcomes and the unfair distribution of resources, power, and wealth across all levels of society. Quandries present, however, through incongruence, vagueness and disparate interpretations of the meaning of equity dilute and fragment efforts across research, policy and practice. Progress on reducing health inequities is, in this context, unsurprisingly irresolute. In this article, we make a case for equity science that reimagines the ways in which we (as researchers, as systems leaders, as teachers and mentors, and as citizens in society) engage in this work. We offer a definition of equity, its determinants, and the paradigmatic foundations of equity science, including the assumptions, values, and processes., and methods of this science. We argue for an equity science that can more meaningfully promote coherent alignment between intention, knowledge and action within and beyond the health sciences to spark a more equitable future.

The interactive effect between economic uncertainty and life history strategy on corrupt intentions: a life history theory approach.

Introduction: Why do some people show more corruption when facing uncertain environment? The present study aimed to give a plausible answer from an evolutionary perspective: this might be rooted in people's different life history strategies (slow vs. fast). Methods: The present study measured the participants' corrupt intentions by a hypothetical scenario and primed the feeling of economic environmental uncertainty by requiring the participants to read economic uncertainty (vs. neutral) materials. Results: It is revealed that the participants with fast life history strategies had stronger corrupt intentions after reading materials about economic uncertainty than reading neutral materials. In addition, the desire for power mediated the interactive effect between life history strategy and economic uncertainty on corrupt intentions for fast life history strategists. Discussion: This finding was discussed for its theoretical and practical implications from the perspective of life history theory.

Wind energy resource assessment and wind turbine selection analysis for sustainable energy production.

The objective of this study is to perform an analysis to determine the most suitable type of wind turbine that can be installed at a specific location for electricity generation, using annual measurements of wind characteristics and meteorological parameters. Wind potential analysis has shown that the analyzed location is suitable for the development of a wind farm. The analysis was carried out for six different types of wind turbines, with a power ranging from 1.5 to 3.0 MW and a hub height set at 80 m. Wind power potential was assessed using the Weibull analysis. The values of the scale coefficient c were determined, and a large monthly variation was observed, with values ranging from 1.92 to 8.36 m/s and an annual value of 4.95 m/s. Monthly values for the shape coefficient k varied between 0.86 and 1.53, with an annual value of 1.07. Additionally, the capacity factor of the turbines was determined, ranging from 17.75 to 22.22%. The Vestas turbine, with a nominal power of 2 MW and a capacity factor of 22.22%, proved to be the most efficient wind turbine for the specific conditions of the location. The quantity of greenhouse gas emissions that will be reduced if this type of turbine is implemented was also calculated, considering the average CO2 emission intensity factor (kg CO2/kWh) of the national electricity system.

Enhancing Ethiopian power distribution with novel hybrid renewable energy systems for sustainable reliability and cost efficiency.

Economic development relies on access to electrical energy, which is crucial for society's growth. However, power shortages are challenging due to non-renewable energy depletion, unregulated use, and a lack of new energy sources. Ethiopia's Debre Markos distribution network experiences over 800 h of power outages annually, causing financial losses and resource waste on diesel generators (DGs) for backup use. To tackle these concerns, the present study suggests a hybrid power generation system, which combines solar and biogas resources, and integrates Superconducting Magnetic Energy Storage (SMES) and Pumped Hydro Energy Storage (PHES) technologies into the system. The study also thoroughly analyzes the current and anticipated demand connected to the distribution network using a backward/forward sweep load flow analysis method. The results indicate that the total power loss has reached its absolute maximum, and the voltage profiles of the networks have dropped below the minimal numerical values recommended by the Institute of Electrical and Electronics Engineers (IEEE) standards (i.e., 0.95-1.025 p.u.). After reviewing the current distribution network's operation, additional steps were taken to improve its effectiveness, using metaheuristic optimization techniques to account for various objective functions and constraints. In the results section, it is demonstrated that the whale optimization algorithm (WOA) outperforms other metaheuristic optimization techniques across three important objective functions: financial, reliability, and greenhouse gas (GHG) emissions. This comparison is based on the capability of the natural selection whale optimization algorithm (NSWOA) to achieve the best possible values for four significant metrics: Cost of Energy (COE), Net Present Cost (NPC), Loss of Power Supply Probability (LPSP), and GHG Emissions. The NSWOA achieved optimal values for these metrics, namely 0.0812 €/kWh, 3.0017 × 106 €, 0.00875, and 7.3679 × 106 kg reduced, respectively. This is attributable to their thorough economic, reliability, and environmental evaluation. Finally, the forward/backward sweep load flow analysis employed during the proposed system's integration significantly reduced the impact of new energy resources on the distribution network. This was evident in the reduction of total power losses from 470.78 to 18.54 kW and voltage deviation from 6.95 to 0.35 p.u., as well as the voltage profile of the distribution system being swung between 1 and 1.0234 p.u., which now comply with the standards set by the IEEE. Besides, a comparison of the cost and GHG emission efficiency of the proposed hybrid system with existing (grid + DGs) and alternative (only DGs) scenarios was done. The findings showed that, among the scenarios examined, the proposed system is the most economical and produces the least amount of GHG emissions.

Utilizing support vector machines to foster sustainable development and innovation in the clean energy sector via green finance.

As the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. in order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. through the use of Support Vector Machine (SVM) Multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. with wind power base a as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terAs the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. In order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. Through the use of Support Vector Machine (SVM) multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. With Wind Power Base A as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terms of the number of units, internal rate of return within the project, and annual average equivalent utilization hours at Wind Power Base A. The number of units decreases to 5860, with total purchase and transportation costs remaining basically unchanged, the internal rate of return increases to 9.3%, and annual equivalent utilization hours increase to 2044 h. Energy consumption and CO2 emissions are significantly reduced, with energy consumption decreasing by 0.68 × 109 kgce and CO2 emissions decreasing by 1.29 × 109 kg. The optimization effects are mainly concentrated in the production and installation stages, with emission reductions achieved through the recycling and disposal of materials consumed in the early stages. In terms of investment benefits, environmental benefits are enhanced, with a 13.93% reduction in CO2 emissions. Moreover, there is improved energy efficiency, with the energy input-output ratio increasing from 7.73 to 9.31. This indicates that the Wind Power Base A project has significant environmental and energy efficiency advantages in the clean energy industry. This work innovatively provides a comprehensive assessment and optimization scheme for clean energy projects and predicts the profitability of Wind Power Base A using SVM multi-factor models. Besides, this work optimizes key parameters of the project using a bi-level multi-objective approach, thus comprehensively improving the investment return and utilization efficiency of the wind power base. This work provides innovative methods and strong data support for the development of the clean energy industry, which is of great significance for promoting sustainable development under the backdrop of green finance.

Sustainable coffee: A review of the diverse initiatives and governance dimensions of global coffee supply chains.

With a global footprint of 10 million hectares across 12.5 million farms, coffee is among the world's most traded commodities. The coffee industry has launched a variety of initiatives designed to reduce coffee's contribution to climate change and biodiversity loss and enhance the socio-economic conditions of coffee producers. We systematically reviewed the literature on the sustainability and governance of coffee production and developed a typology of eleven sustainability initiatives. Our review shows that coffee sustainability research has focused primarily on the economic outcomes of certification schemes. The typology expands our knowledge of novel sustainability initiatives being led by coffee farming communities themselves, allowing for an improved consideration of power dynamics in sustainability governance. Sustainability initiatives governed by local actors can improve sustainability outcomes by empowering local decision makers to assess direct risks and benefits of sustainable practices to the local environment, economy, and culture.

Risk assessment of deep-sea floating offshore wind power projects based on hesitant fuzzy linguistic term set considering trust relationship among experts.

The development of deep-sea floating offshore wind power (FOWP) is the key to fully utilizing water resources to enhance wind resources in the years ahead, and then the project is still in its initial stage, and identifying risks is a crucial step before promoting a significant undertaking. This paper proposes a framework for identifying risks in deep-sea FOWP projects. First, this paper identifies 16 risk criteria and divides them into 5 groups to establish a criteria system. Second, hesitant fuzzy linguistic term set (HFLTS) and triangular fuzzy number (TFN) are utilized to gather and describe the criterion data to ensure the robustness and completeness of the criterion data. Third, extending the method for removal effects of criteria (MEREC) to the HFLTS environment through the conversion of TFNs, under the influence of subjective preference and objective fairness, a weighting method combining analytic network process (ANP) and MEREC is utilized to calculate criteria weights, and the trust relationship and consistency between experts are used to calculate the expert weights to avoid the subjective weighting given by experts arbitrariness. Fourth, the study's findings indicated that the overall risk level of the deep-sea FOWP projects is "medium." Fifth, sensitivity and comparative analyses were conducted to test the reliability of the assessment outcomes. lastly, this research proposes risk management measures for the deep-sea FOWP project's establishment from economic, policy, technology, environment, and management aspects.

Disparities with global standards about growth references of mid-upper arm circumference-for-age for Pakistani children aged 6-60 months.

AIM: Growth reference values about mid-upper arm circumference (MUAC) are vital for assessing children's nutritional status. However, Pakistan lacks these reference values and growth charts. This study aims to develop these for children aged 6-60 months and compare them with global standards. METHODS: The data were acquired from the 2018 National Nutrition Survey of Pakistan, which was conducted by the United Nations Children's Fund (UNICEF) during 2018-2019. The final study cohort comprised 57 285 children, with 51% being boys. Percentile values and charts for MUAC-for-age were developed using generalised additive models for location, scale and shape with the Box-Cox power exponential distribution. RESULTS: The mean MUAC was 14.21 cm (±2.07 cm) and 14.13 cm (±2.12 cm) for the boys and girls, respectively. At 60 months of age, the P3 and P97 percentiles for girls were slightly higher than those for boys. The median percentiles of Pakistani children were smaller than the World Health Organisation 2007 standards and with international references. CONCLUSION: We observed disparities in MUAC-for-age growth references among Pakistani children compared to global standards, highlighting regional, age and gender variations. This underscores the need for developing countries like Pakistan to establish their growth references.

A life cycle cost analysis of different shore power incentive policies on both shore and ship sides based on system dynamics and a Chinese port case.

Shore power (SP) is widely recognized as an efficient strategy for reducing air pollution in port areas. Unfortunately, the adoption of SP has been relatively low, resulting in limited emission reductions and financial losses. To address these challenges, this paper focuses on enhancing the utilization rate of SP, which is meaningful for emission control and environmental protection. This paper combines system dynamics with a study of the benefits of SP, which bridges the research gap to some extent. We propose a system dynamics model that assesses the impact of various incentive policies on the economic and environmental benefits of SP. The model considers the life cycle cost and comprises four subsystems. By conducting a case study on Nansha Port, we find that price subsidies are more effective than construction subsidies in overcoming economic barriers. Furthermore, we observe that the overall economic benefits only increase when the electricity price decreases. This is because lowering the electricity price enhances the profitability of ships without negatively affecting port revenue. Additionally, it is the proportion of the electricity price and service price that determines the overall economic benefits, rather than the SP price itself. Hence, it is recommended to provide preferential subsidies for the electricity price.

Ultrastrong and Reusable Solar‒Thermal‒Electric Generators by Economical Starch Vitrimers.

In frigid regions, it is imperative to possess functionality materials that are ultrastrong, reusable, and economical, providing self-generated heat and electricity. One promising solution is a solar‒thermal‒electric (STE) generator, composed of solar‒thermal conversion phase change composites (PCCs) and temperature-difference power-generation-sheets. However, the existing PCCs face challenges with conflicting requirements for solar‒thermal conversion efficiency and mechanical robustness, mainly due to monotonous functionalized aerogel framework. Herein, a novel starch vitrimer aerogel is proposed that incorporates orientational distributed carboxylated carbon nanotubes (CCNT) to create PCC. This innovative design integrates large through-holes, mechanical robustness, and superior solar‒thermal conversion. Remarkably, PCC with only 0.8 wt.% CCNT loading achieves 85.8 MPa compressive strength, 102.4 °C at 200 mW cm-2 irradiation with an impressive 92.9% solar-thermal conversion efficiency. Noteworthy, the STE generator assembled with PCC harvests 99.1 W m-2 output power density, surpassing other reported STE generators. Strikingly, even under harsh conditions of -10 °C and 10 mW cm‒2 irradiation, the STE generator maintains 20 °C for PCC with 325 mV output voltage and 45 mA current, showcasing enhanced electricity generation in colder environments. This study introduces a groundbreaking STE generator, paving the way for self-sufficient heat and electricity supply in cold regions.