This research aims to evaluate the effect of the global innovation index (GII), urban population (UP), rural population (RP), social globalization index (SGI), and Demographic Pressures (DP) on agriculture value added (AVA). The samples were collected from The Global Economy relating to four countries, namely, China, Indonesia, USA, and India during 2013-2020. The collected data were analyzed through comparative panel regression. The results show that, in the long term, the increase in UP is significantly correlated with AVA in China, Indonesia, and India. Then, an increase in RP is significantly associated with AVA in India, and SGI exerts a significant effect on AVA in the USA. In the short term, every increase in GII has a significant effect on AVA in Indonesia and the USA. Furthermore, RP has a significant impact on AVA in China. A case study in India, reported that a decrease in SGI and DP significantly affect AVA. Meanwhile, the short-term DP growth greatly influences the AVA in the USA. The novelty of this research lies in the finding that the agricultural sector's tendency expands our knowledge of how many agricultural clusters in rural communities change their functions as a large number of rural residents relocate to metropolitan areas. Further, the debate over the HR industry's level of competition does not create job possibilities. In China, Indonesia, and the USA, DP enhances AVA. Stakeholders are required to reduce housing density through innovative agriculture concepts that are more humanistic and creative, even though DP only suppresses AVA in India.

The industrial revolution is now entering its fourth phase. The rapid development of science and technology has a big impact on human life. Many conveniences and innovations are obtained with the support of digital technology. Education today is a phenomenon that responds to the needs of the industrial revolution with new curriculum adjustments according to the COVID-19 situation. The curriculum is able to open the window of the world through its grasp for example utilizing the internet of things (IoT). On the other hand, teachers also get more references and teaching methods. For the education sector, the presence of industry 4.0 is expected to be able to realize smart education through enhancing and equalizing the quality of education, expanding access, and relevance in realizing world-class. To realize this learning interaction is carried out through blended learning, project-based learning, and flipped classroom (public interaction and digital interaction) so that these challenges can be overcome in order to prevent the impact of pandemic transmission.

The cause of market failure is the lack of synchronization between supply and demand, or vice versa. Initially, corn was considered as a substitute and a complementary commodity for food, but now it has turned into a basic need for people in Indonesia, especially when they celebrate the new year. The main aim of this study is to examine the interaction between the demand side and the supply side. The study was carried out by inviting 9,850 respondents in Samarinda City to be surveyed regarding interest, tradition, taste, price, opportunity, profit, production cost, and distribution. Empirical testing was used to interpret the data, including correlation, reliability, and validity. Constructive validity was found in the market behavior function, where the indicators of consumer demand (p < 0.01) and produce supply (p < 0.01) have a significant effect. Also highlighted were production cost and distribution, both of which have a positive channel and are closely related to the other six indicators. The consistent performance of production cost and distribution supported further evidence providing stability of measurement results. Additionally, tradition and opportunity also produced high coefficients in the reliability test. We can further examine empirical results by including other dimensions, such as social, psychological and individual factors. In addition, diagnostic transformations need to be highlighted where market trends can change along with the growth of other commodities.

The problem of energy for fishermen is indeed very complex. When fuel prices rise, it is very worrying that the poor in Indonesia will increase. The study aimed to find out the development of fishermen, as well as the benefits of using Brown's Gas Electrolyzer (BG-E) on a fishing boat in Indonesia. This type of data is secondary and sourced from second parties (Government Office and others). Analysis, synthesis methods, and alternative problem solving are prepared based on data, information, and observations to solve fisherman's problems based on research objectives. The application of BG-E technology can be applied to small-scale fishing boats, is Outboard Motorboats and 5-30 GT Motorboats. BG-E is made according to the ship's power requirements. The way it works through an electrolysis process to decompose compounds in the form of solutions melts, or ordinary liquids by the electric current flowing through the compound. From the results of the electrolysis process, it produces oxyhydrogen which will react with gasoline in the boat engine. This reaction will produce an explosion to move the piston on the ship's engine so that the engine can live. With the implementation of BG-E, it can save fuel oil consumption and improve the welfare of Indonesian fishermen. The originality of the research is related to the method used in the framework of estimating Fishermen fuel savings in Indonesia, so that the study will become novelty in the future. Contribution/ Originality:The study aimed to find out the development of fishermen, as well as the benefits of using Brown's Gas Electrolyzer (BG-E) on a fishing boat in Indonesia.

The concept of green growth is one part of the realization of sustainable development. To support this mission, Indonesia is taking part in global change by accelerating the development programs contained in the SDGs. We need to study Green Growth (GG) which is determined by the empowerment of the energy sector such as Source of Electric Lighting (SEL), Renewable Energy Mix (REM), and Primary Energy Intensity (PEI) in Indonesia. Timeseries data were analyzed using Ordinary Least Squares (OLS) modeling in the 2015-2024 period. The result, of the three targeted hypotheses, only two can be accepted which are explained by SEL and PEI have a positive effect on GG. In another exploration, one hypothesis that was rejected was that REM had a negative effect on GG. The implications of this study are brought to the attention of our findings that have raised important points, especially in the SDGs document on the energy sector.