This article develops a dynamical systems model to simulate the flows and loss mechanisms of fresh fruit bunches (FFB). Formulated as a system of differential equations, the model represents FFB stocks and associated flows while incorporating explicit loss terms. The existence of a unique and globally stable steady state, which represents the equilibrium proportion of total FFB loss, was proven. Subsequently, the parameterisation approach combines small-scale survey data from 27 smallholders in southern Thailand, which has the highest concentration of oil palm plantations, with assumption-driven values based on official statistical records, academic literature, and expert insights. Scenario and sensitivity analyses were conducted to identify the key drivers of FFB loss. This model-based analysis indicates that the most effective strategies for reducing FFB loss are those that focus on improving harvest efficiency while simultaneously optimising in-field operational practices, thereby supporting more sustainable palm oil supply chain management.

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Packaging waste produced by tourist accommodation significantly contributes to environmental impacts. Understanding packaging waste generation patterns and waste flows allows for the identification of major waste streams and the factors influencing their occurrence. This study aims to assess the patterns and flows of packaging waste generation in the tourist accommodations, focusing specifically on five provinces in North-East Thailand and five types of packaging materials. Investigating the geographical pattern, explore the complexities of consumer behavior and examine the dynamics of waste collection patterns. The questionnaire has been developed to collect data regarding packaging waste across 35 accommodations. The results indicate that plastic waste is the most predominant type, totaling around 300 kg per day, with plastic bottles for drinking water being the major component. However, the highest type of waste packaging is glass bottles, totaling approximately 266 kg per day. In geographical pattern, Loei generates the highest amount of packaging waste, averaging 0.41 kg per guest per day, while Udon Thani generates the least, an average of 0.15 kg per guest per day. The main reason is that Loei has more attractions, whereas Udon Thani offers a wider variety of restaurants, leading to more packaging waste generated outside of the accommodations. Consumer behavior patterns show that glass bottles and seasoning packets are the most commonly discarded waste items in kitchens, dining rooms and hospitality rooms, where much of the packaging waste is collected and sorted for recycling. A study of packaging waste flow revealed that the kitchen is the main source of packaging waste. Additionally, most packaging is sold or returned to the suppliers for recycling. Tourist accommodation should focus more on diverting packaging waste from landfills to promote sustainable waste management. This study provides guidelines for setting concrete targets to reduce packaging waste in tourist accommodation for tourist sustainability.

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Thailand’s steady growth of urban areas and industrial estates, the construction sector needs to adopt a low-carbon circular economy (CE) model that emphasizes material circularity and resource efficiency. This research aims to measure the low-carbon CE of the construction industry through significant representative products, life cycle assessment and material circularity indicator (MCI) were measured on significant representative products materials were evaluated. In the results of the study, it was found that the MCI and GHGs revealed the following results: 1 ton of construction steel products = 0.73 and 2.32 kgCO2/ton, 1 bag (50 kg) of mortar and cement products = 0.17 and 16.92 kgCO2/bag, 1 m3 of ready-mixed concrete at compressive strength 240 kilograms per square centimeter = 0.11 and 253.63 kgCO2/m3, 1 m3 of wood and composite wood products = 0.17 and 745.84 kgCO2/bag, also 1 m2K/W (meters squared Kelvin per Watt) of glass wool insulation = 0.50 and 1.63 kgCO2/m2K/W, respectively. These values are indicated as national baselines for monitoring CE performance that contribute to the industry’s long-term viability and turn to sustainability in Thailand through the key strategic issues in Thailand’s CE and low-carbon society. The GHGs reduction of 11 million tons is expected, which can also increase the 10% of material circularity, also the estimation of the economic value, by considering the value added from material reduction and the price of carbon credit from construction and demolition waste reduction affect the Thai construction industry by approximately 67 million dollars.

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Whilst the world population is increasing, consequently, the demand for food and agricultural products also raised. Farmers turn to monoculture for high the productivity of agricultural products with limited arable land and capital for gaining better income; however, farmers still have low incomes. Therefore, evaluating the potential of agricultural spatial is one of the significant factors influencing agricultural productivity also the income of farmers such as crop pattern, agricultural practice, and material used for the cultivation with the most efficient use of limited resources. This research is to propose an appropriate crop pattern based on plant spacing for allocating plantation areas through Linear Optimization technique in agroforestry, which five plant groups are composed of soil-improving crops, fruit crops, vegetables and herbs, cash crops and forests, for the optimization of farm productivity in 1 rai (1 rai = 0.16 hectares) of the arable area by determining the net income. The results have shown that the optimal agricultural pattern (Pattern 1) for planting the soil-improving crops, fruit crops and cash crops throughout the area, while forest, vegetables and herbs are around area. This pattern provides the numbers of crop tress for each type with maximum farmer’s income.

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Maize is widely produced because it is the essential raw material for animal feed industries. Maize is one of the drought-tolerant plants; however, it needs to consume water for cultivation like Crop Water Requirement (CWR). In fact, CWR estimation is used to investigate sufficient agricultural water consumption. In addition, CWR estimation can presently calculate from the global crop coefficient from the FAO Penman-Monteith. On the other hand, when this crop coefficient of maize in each area is not similar under local conditions, the global crop coefficient is inconsistent with the specific area. Therefore, this study aims to estimate the crop coefficient of maize under local conditions under the study area. The crop coefficient (Kc) of maize is calculated by using remote sensing technique in the maize crop through image processing for the Normalized Difference Vegetation Index (NDVI) as NDVI-Kc to estimate the CWR and then CWR using NDVI-Kc is compared with CWR from Royal Irrigation Department. The study area is Mae Chaem district in Chiang Mai Province because this area is a steep slope and lack access to irrigation. Results reveal the amount of CWR through NDVI-Kc in this study area is 471.50 m3/ rai, which is similar to CWR of the Royal Irrigation Department around 550 m3/ rai. In other words, the accuracy assessment of this result is 86 % as percent accuracy. NDVI-Kc can assess CWR to plan water management for maize in the steep slope which is difficult to access water source.

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