Articles

Transforming Tourism in Indonesia: Advancing a Green Economy for Sustainable Development and Job Creation

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The primary objective of this research is to analyze the transformation of tourism in Indonesia towards a green economy, focusing on creating sustainable and environmentally friendly job opportunities. This study employs a qualitative approach, utilizing policy analysis and case studies of high-priority destinations like Lake Toba and Labuan Bajo to assess the integration of green economy principles into tourism practices. The findings indicate that investments in public transportation infrastructure and green technologies, such as electric vehicles, enhance tourist experiences while significantly reducing carbon emissions. Additionally, implementing carbon pricing policies is expected to accelerate economic recovery in the post-pandemic context by promoting responsible tourism. Collaboration among government entities, academia, and society emerges as crucial for creating green jobs that benefit all stakeholders. The research underscores the importance of participatory approaches in sustainable tourism development to raise awareness about resource conservation. Ultimately, the transformation towards a green economy in Indonesia’s tourism sector not only aims to attract more tourists but also aligns with global commitments to combat climate change, fostering a resilient industry that benefits local communities and the environment.

EcoCycle: A Deep Learning-Based Waste Categorization and Management System for Sustainable Smart Cities

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Waste management is a critical environmental and economic issue worldwide. Existing waste segregation ac- tivities are inefficient, resulting in high landfill contributions and environmental contamination. In this paper, an artificial intelligence-based waste categorization and management system, EcoCycle, is proposed that utilizes deep learning models like VGG16, ResNet50, and DenseNet121 for automatic classification of waste materials. EcoCycle is equipped with a gamification system based on mobile, a marketplace for recyclables supported by blockchain, and an IoT-based network of intelligent bins for real-time monitoring. Experimental results show 92.36% classification accuracy with DenseNet121, which is improved compared to other implementation results. User survey with 500 users shows a 98% positive effect on user experience and increased awareness about sus- tainability issues. The proposed system contributes significantly towards processes related to circular economies and the goals of smart city initiatives, and it has high global applicability potential for urban waste management systems.

The Degrowth of Tourism Industry in the Island of Crete, Greece. Is it Desirable and Feasible?

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The concept of degrowth has been developed recently as an alternative paradigm to continuous and unlimited growth which prevails nowadays. The degrowth of tourism industry has been proposed in overcrowded tourism destinations balancing the satisfaction of tourists and local residents with the sustainability of the natural ecosystems. The island of Crete, Greece is located in eastern Mediterranean basin hosting an increasing number of visitors every year. The tourism industry in the island is well developed having a high economic impact. The prosperous tourism industry in Crete has surpassed the carrying capacity of the island threatening its fragile ecosystems causing phenomena of overtourism with undesired and harmful impacts in the local societies. The degrowth of the local tourism industry can be conceived as an alternative paradigm of the current growth model of tourism which threatens its long-term sustainability. It rejects the current model of continuous and unlimited growth of tourism promoting the values of locality, development of small-scale enterprises, quality of life, environmental sustainability, reduction of carbon emissions, decommodification of tourism activities and smaller production and consumption. The adoption of tourism degrowth in Crete requires the mobilization and the active participation of the multiple stakeholders of the tourism industry in the island while it promotes their long-term interests which are currently threatened by overtourism, climate crisis and overconsumption of the limited natural resources.

Sustainability Leadership and Employee Engagement: A Key Driver of Productivity in Indonesian Companies

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The background of this research is the challenge faced by Indonesia’s economic development in achieving sustainable growth without increasing carbon emissions. This study aims to analyze the influence of leadership and the implementation of sustainability principles on productivity, with employee engagement as a mediator in public and private companies in Indonesia.

The research adopts a quantitative approach, utilizing data analysis through the Structural Equation Modelling Partial Least Square (SEM PLS) method. The study sample comprises 110 respondents from public and 110 respondents from private companies, with data collected through questionnaire surveys with G*Power 88%. Research variables include sustainability leadership, implementation of sustainability principles, employee engagement, and productivity.

The findings reveal that employee engagement significantly influences employee productivity and mediates the effect of sustainability leadership on employee productivity. These findings highlight the critical role of sustainability-based leadership in fostering employee engagement and enhancing productivity.

The study concludes that Indonesian companies, particularly those oriented toward sustainability, need to prioritize the development of sustainability-based leadership and policies to enhance employee engagement, thereby supporting long-term productivity.

Analysis of Sustainable Rice Supply Chain Model Using Supply Chain Operations Reference (SCOR) in Sidenreng Rappang Regency

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Indonesia is a country where most of its population lives from agriculture, making the agricultural industry sector an important industry for people’s lives. One of the commodities in this sector is rice, which is the raw material for rice production. Indonesia is ranked first in ASEAN countries with the highest rice and rice production based on data from the ASEAN Statistical Year Book 2021, where rice production was 55.53 million metric tons in 2020. Sidenreng Rappang Regency is known for its high rice production. The purpose of this study is to analyze whether the sustainable rice supply chain model in Sidenreng Rappang Regency has been running well or not. This study uses the Supply Chain Operations Reference (SCOR) method, and the populations studied are farmers, rice mills, and seller. The first stage of work in this study is to identify levels 1, 2, and 3 of the KPI SCOR for each population. The second phase involves determining the score for level 3. The third stage involves determining the weight of each level. The fourth stage determines the final value of levels 3, 2, and 1 of all populations. Based on the analysis conducted, the total value of supply chain performance for the farmer population was 38.28. Rice milling resulted in a total value of supply chain performance of 53.19. 53.02 was the total value of the seller population and 53.02 was the performance of the supply chain.

Proposed Business Strategy for Implementation of Green Port at Merak Ferry Port to Achieve Sustainability

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Climate change is a challenge for the global world, requiring immediate action to reduce its impact on the environment. Indonesia is a party to the Paris Agreement, where Indonesia is committed to reducing reduce greenhouse gas emissions by 31.89% by 2030. As a state-owned company, PT. ASDP Indonesia Ferry (Persero), which operates in the port and ferry sector, is one of the transportation industry entities in Indonesia that plays a role in supporting this commitment. Merak Ferry Port, the largest port managed by PT. ASDP Indonesia Ferry (Persero) requires the implementation of environmentally friendly port practices to be in line with sustainability goals. This research aims to develop a business strategy for implementing a green port at the Merak Ferry Port to achieve sustainability.  Identify best practices from globally successful green port initiatives, evaluate the status of Merak Ferry Port in terms of sustainability initiatives, and propose actionable strategies to transition towards sustainable port operations. The research methodology includes qualitative analysis by collecting primary data through stakeholder interviews and collecting secondary data from case studies and related documents. Analytical tools such as Benchmarking, PESTEL, VRIO, and Value Chain analysis are used to assess internal and external factors influencing port sustainability. These findings indicate that there is a great opportunity for the Merak Ferry Port to improve environmental performance through the application of renewable energy, energy-saving technology and a comprehensive waste management system. The proposed strategy emphasizes the integration of environmental, economic and social dimensions of sustainability by utilizing the Triple Bottom Line approach. The green port implementation strategy implementation plan outlines specific actions, timelines, and resource allocation to ensure successful implementation of environmentally friendly port practices. This study contributes to knowledge about sustainable port management and provides business solutions for PT. ASDP Indonesia Ferry (Persero) to increase operational efficiency and concern for the environment. The results of this research underscore the importance of aligning business strategy with a commitment to national and international sustainability to achieve long-term viability and competitiveness in the port and ferry industry.

Implementation of ESG as a Strategy for Business Sustainability in a Public-Listed Tobacco Company in Indonesia

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Environmental, Social, and Governance (ESG) has been introduced to the business communities for the past two decades. It has grown in importance as a framework for measuring a company’s sustainability and as a guide for investment decision-making. In Indonesia, the publicly listed companies at the Indonesia Stock Exchange (IDX) have been required to implement and report ESG practices since 2021 through a regulation issued by the Financial Services Authority (OJK) in 2017. Among those is a tobacco company operating in Indonesia, PT HM Sampoerna Tbk. (Sampoerna/The Company/IDX: HMSP) that has been an affiliate of an international tobacco company, Philip Morris International (PMI), since 2005. The tobacco industry’s ESG implementation is particularly interesting due to the adverse externalities generated by its products, which is stated by WHO as one of the biggest issues for public health. This research focuses on analyzing the company’s ESG initiatives and creating improvement strategies in the context of a company operating in the tobacco industry with the objective of maximizing the role of ESG implementation to ensure business sustainability.

Advanced TRST01 ESG Scoring Model with Beta Based Financial Metrics and Machine Learning Techniques

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In the current corporate world, assessing a company’s sustainability performance is very important for investors, stakeholders, and policymakers. The TRST01’s ESG (Environmental. Social and Governance) Scoring Model introduces an innovative approach integrating beta-based financial metrics with advanced machine learning techniques to comprehensively evaluate ESG credentials. This study demonstrates the development and application of the TRST01’s ESG scoring model, which leverages data from the most reputable sources such as MSCI and S&P Global to ensure its reliability and accuracy. The model’s unique methodology involves calculating country-specific beta values to normalize carbon emission data, thereby providing a standardized metric for meaningful comparisons across countries. Further, ESG scores are adjusted using both country and company beta values to reflect specific risk exposures, enhancing the precision and relevance of the assessments. The model ensures robust input data quality, by taking Market capital, Scope 1, Scope 2, industry wise data and beta values as predictors through extensive data preprocessing and encoding categorical variables for top 1000 listed companies. A comparative analysis of Traditional model such as Simple Linear Regression (SLR) and multiple Machine Learning (ML) models, including Gradient Boosting (GB), Support Vector Regression (SVR), and Random Forest (RF), demonstrates that the Gradient Boosting model achieves superior performance with minimal overfitting and consistent prediction accuracy. The study employs a comprehensive evaluation framework using various metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared, supplemented by detailed visualizations of actual vs. predicted values, residuals, and error distributions. This research underscores the significance of incorporating advanced financial metrics and machine learning techniques in ESG assessments, providing a reliable, accurate, and holistic framework for understanding corporate sustainability. The TRST01 ESG Scoring Model sets a new standard in sustainability evaluation, offering valuable insights for stakeholders committed to integrating sustainability into core business strategies.

Predicting a Higher Heating Value for Torrefied Kesambi Leaf Biobriquettes through Ultimate Analysis

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The escalating global pursuit of sustainable energy solutions has led to the emergence of biomass-derived fuels, such as biobriquettes, as feasible substitutes for traditional fossil fuels. Kesambi leaves, which are abundant in Southeast Asia and boast a high calorific value, represent a promising prospect for the production of biobriquettes. In this investigation, a conclusive analytical method is employed to construct a predictive framework for estimating the Higher Heating Value (HHV) of torrefied kesambi leaf biobriquettes. By incorporating ash content (PS), volatile matter (BR), carbon (C), hydrogen (H), and oxygen (O) percentages, alongside experimental HHV data, through multiple linear regression and elemental composition data acquired from proximal analysis, the model aims to forecast HHV. The model’s modest positive Mean Bias Error (MBE) and satisfactory Root Mean Square Error (RMSE) suggest a good fit. The substantial R-squared value indicates the model’s capability to adeptly capture HHV variability. Ultimately, this approach grounded in fundamental principles contributes significantly to the sustainable exploitation of biomass resources by providing a pragmatic and effective technique for predicting HHV for kesambi leaf biobriquettes.