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As global, regional, and national concerns about the future of human life and nature continue to grow, researchers in the field of marketing have contributed to this discourse by conducting various scientific studies on sustainability from a marketing perspective. This research aims to provide a comprehensive overview of green marketing research using bibliometric analysis and network visualization techniques. The bibliometric analysis is applied to provide complete understanding of the core issues, directions, trends, and opportunities in green marketing research. The steps of bibliometric analysis include data collection, article scoping, metadata compilation, and data visualization using Vosviewer. Based on data from scopus.com, a search for green marketing publications yielded 610 articles. Using bibliometric parameters such as authors, citations, keywords, and country of publication, the research presents a systematic research method for the analysis process. The analysis results reveal that the classification of green marketing research consists of 6 clusters focusing on consumption behavior, behavioral impact, firm innovation and performance, business development, sustainability, and economics.

Energy efficiency and conservation are pivotal in addressing global energy challenges and promoting sustainability. This paper presents a comprehensive bibliometric analysis spanning 1976 to 2023, aiming to reveal the nuanced dynamics of the field. Utilizing the Scopus database and bibliometric analysis tools like VOSviewer, we explored publication trends, platform rankings, publication types, global distribution, and interconnected research themes. The dominance of top platforms like Energy and Journal of Cleaner Production underscored their significance, with insights into the role of conference proceedings and specialized platforms like Energy Efficiency. Journal articles prevailed, reflecting the academic community’s commitment to peer-reviewed channels, while diverse publication forms and subject areas highlighted a multifaceted approach. The global landscape showcased the United States’ leadership, with impactful contributions from Indonesia, fostering collaboration between developed and developing nations. VOSviewer visualizations revealed interconnected research themes, emphasizing the interdisciplinary nature of climate change and sustainable development. This study offers valuable insights for researchers, policymakers, and practitioners, guiding them to understand historical trends, identify influential platforms, recognize interdisciplinary patterns, and foster global collaborations to shape a more sustainable energy future.

This research aims to comprehensively map the stages at which food materials are discarded as food loss and food waste, drawing insights from existing literature. This study employs a systematic literature review methodology-the works of literature collected from reputable international journal databases. The findings of this investigation reveal a diverse array of research endeavors within the realm of food waste management. Researchers have undertaken endeavors to categorize different types of food waste, while others have delved into examining food resilience and waste generation in agricultural phases or amongst farmers. Distinct studies have concentrated on food resilience and waste issues encompassing traditional market traders. The academic community has also directed attention towards food waste in harvest, retail sales, followed by the hospitality industry, restaurant management, and household settings. The outcomes of this systematic literature review offer a foundational framework for delineating food waste patterns across agricultural, traditional market, retail, restaurant, hotel, and household contexts. Subsequent empirical research is poised to focus on categorizing food waste by type, identifying root causes, exploring mitigation efforts, and elucidating waste management strategies enacted by farmers, traditional market traders, retail stores, restaurants, hotels, and households.

The solar power plant is a rapidly growing renewable energy source that has a potential role in reducing climate change and replacing fossil fuels. Estimation of the power generated by a solar power plant is required to determine the energy supply. Unfortunately, the solar power generated is highly uncertain due to highly dependence to nature, such as solar radiation and weather. This makes the estimation of solar power generation to be very difficult. This study presents a development of machine learning to model a solar power plant for estimating the generated power. The machine learning is developed by implementing the k-NN algorithm. A data set of power generated in a solar power plant is applied to build the machine learning. The development resulted in a machine learning that models the solar power plant. Simulation test result show the machine learning was able to estimate the solar power generated with an accuracy of 69.6%. The developed model is very useful to estimate potential of solar power resource in a region. The developed model is very useful in feasibility studies to estimate the potential of solar power resources in an area.

Black and red rice contain nutritional values not found in white rice. They contain anthocyanins which are beneficial to health. Drought stress can potentially reduce rice yield. So, it is necessary to develop local black and red rice into tolerant cultivars. This study used a completely randomized design (CRD) with cultivar factor (V) and drought stress level (K). Drought stress treatment was carried out when the plants were 35-95 DAP (days after planting) using the modified gravimetric method. Plant destruction was conducted at 7 and 21 DAT (days after treatment) to measure the growth analysis component. The observation of the growth analyses was plant dry weight, leaf area (LA), specific leaf area (SLA), relative growth rate (RGR), and net assimilation rate (NAR). Agronomic characters by observing shoot-root ratio and flowering age. The results showed drought stress decreased plant dry weight, leaf area, and relative growth rate on Sragen black rice. Jelitheng Karanganyar showed high RGR and NAR under drought stress and the fastest flowering age response. Wonogiri black rice gave the growth response by reducing specific leaf area. The highest shoot-root ratio was shown on Wonogiri black rice.