Indonesia occupies a strategically important position in the global energy transition because it is both a major producer of transition minerals and a country pursuing domestic decarbonization. This article develops an integrated sustainability framework to examine how Indonesia’s mining sector can contribute to a just and sustainable energy transition without reproducing carbon-intensive, socially uneven, and weakly governed development pathways. The study adopts a mixed-method design that combines a targeted literature review, policy analysis, and triangulation of secondary data from official and authoritative sources on mineral production, electricity generation, national energy planning, and climate commitments. The results identify four interdependent sustainability tensions that shape the sector’s transition role: value-added industrialization versus decarbonization, investment acceleration versus governance quality, export competitiveness versus ecological integrity, and national strategic gains versus local distributive justice. In response, the article proposes an integrated framework structured around four pillars: environmental integrity, social justice and inclusion, economic transformation, and adaptive governance. The framework is translated into five policy pathways: decarbonizing mine and smelter power supply, strengthening environmental, social, and governance (ESG)-linked permitting and monitoring, expanding local value capture and community safeguards, aligning mineral strategy with electricity and climate planning, and institutionalizing transition metrics for accountability. The article concludes that Indonesia’s mining sector should not be evaluated solely through output growth or downstream investment, but through its capacity to deliver low-carbon industrial value, equitable development, and credible environmental stewardship. The framework contributes a policy-relevant tool for governments, firms, and researchers seeking to govern critical-mineral expansion in ways that support long-term sustainability and a just transition.
This study develops a life-cycle engineering-management framework for intelligent systems in industrial settings and examines its applicability through a descriptive comparison of four publicly documented cases: Siemens, Atlas Copco, Vallourec, and thyssenkrupp Materials Services. Peer-reviewed studies published in 2020–2025 and official corporate disclosures published in 2019–2025 were screened using explicit relevance and traceability criteria. A structured extraction matrix recorded the industrial context, technology, deployment area, life-cycle stage, responsible actors, intended function, and availability of performance data. Inferential statistics and author-generated estimates were not used because the public sources did not provide replicated observations, consistent baselines, or common denominators. The cases document heterogeneous systems: a generative artificial-intelligence assistant for industrial engineering, connected compressor monitoring, digital traceability and operational support for tubular products, and artificial-intelligence-supported materials logistics. The evidence supports comparison of disclosed functions and management requirements, but it does not support causal claims or rankings based on return on investment, downtime, quality, energy, emissions, or workforce outcomes. The resulting framework links planning, design, integration, operation, and upgrade or retirement to a management decision, systems-engineering task, responsible actor, indicator, and implementation risk. It provides a reproducible basis for future plant-level evaluation while keeping conclusions within the limits of public secondary data.
Geothermal brine retains considerable thermal energy before reinjection, but its potential for additional power generation is not always fully utilized. Low-temperature solar preheating offers a possible route to increase heat recovery without altering the geothermal source conditions. This study investigates the integration of a field-tested trickle solar collector as a preheater for a small-scale geothermal organic Rankine cycle (ORC), with particular attention to energy performance, exergy efficiency, heat-transfer feasibility, and working-fluid selection. A thermodynamic model was developed using experimental collector data and a geothermal brine stream entering at 188 ℃ and leaving at the 90 ℃ reinjection limit. The brine mass flow rate was fixed at 1.690 kg/s, corresponding to a pinch-feasible 100-kW n-pentane reference cycle. Heat-transfer feasibility was evaluated over the complete counter-current temperature profile using a minimum approach temperature of 10 K. The tested collector produced an average useful heat output of 752.4 W per module and reached a maximum outlet temperature of 51.5 ℃. A field of 88 modules, with a total aperture area of 91.52 m$^2$, supplied 45.024 kW of useful solar heat to the preheater. For the n-pentane cycle, solar preheating increased the net power output from 100 to 106.473 kW while the thermal efficiency remained at 14.377%. The exergy efficiency increased from 51.393% to 53.705%, and the minimum temperature approach remained feasible at 10.120 K. Under the same brine and pinch constraints, R245fa produced the highest net power of 110.961 kW, closely followed by R1233zd(E) at 110.547 kW. Preliminary heat-exchanger sizing yielded a logarithmic mean temperature difference of 12.757 K, a UA value of 3.529 kW/K, and a required heat-transfer area of 7.06–11.76 m$^2$. The results indicate that trickle collectors are better suited to low-temperature preheating than direct ORC evaporation. This integration provides a technically feasible approach to increasing power recovery from geothermal brine while maintaining the original reinjection temperature and thermodynamic operating limits.
The relationship between fintech partnerships and financial performance has attracted increasing attention as commercial banks increasingly rely on external technology providers to enhance digital capabilities, develop innovative financial products, and improve operational efficiency. This study examined the association between fintech partnerships and perceived financial performance among commercial banks listed on the Nairobi Securities Exchange (NSE) in Kenya, drawing on transaction cost economics (TCE) and a positivist research philosophy. Primary data were collected using structured questionnaires administered to managers responsible for digital transformation, information technology, strategy, and operations across all 11 NSE-listed commercial banks. Of the 110 questionnaires distributed, 84 valid responses were obtained, representing a response rate of 76.4%. The data were analysed using descriptive statistics, Pearson’s correlation analysis, and simple linear regression. A strong and statistically significant positive association was identified between fintech partnerships and perceived financial performance (r = 0.820, p < 0.01). The regression analysis further indicated that fintech partnerships significantly predicted perceived financial performance (R² = 0.672, p < 0.001). These findings suggest that stronger collaboration between commercial banks and fintech firms is associated with improved perceptions of financial performance. In particular, partnerships involving digital payment systems, application programming interfaces (APIs), and joint product development may provide avenues for strengthening banks’ digital capabilities and competitive positioning. However, the effectiveness of such partnerships is likely to depend on appropriate governance structures, risk-management mechanisms, data-security arrangements, and regulatory compliance. The findings provide empirical support for fintech partnerships as a potentially important strategic mechanism through which listed commercial banks in Kenya can respond to technological change while enhancing their perceived financial performance.
Crude oil is a major energy resource; however, oil exploration and production activities may release petroleum hydrocarbons (PHCs), heavy metals, and other contaminants that can adversely affect groundwater quality. This study evaluates the quality of groundwater surrounding oil mining sites in Musi Banyuasin Regency, South Sumatra, Indonesia, using the Water Quality Index (WQI) method. Groundwater samples were collected from 36 sampling points, consisting of 30 study wells located near oil drilling areas and six control wells, with two control wells in each of the three sub-districts: Sanga Desa, Babat Toman, and Lawang Wetan. The analyzed parameters included pH, temperature, total dissolved solids (TDS), turbidity, color, and heavy metals, including copper (Cu), nickel (Ni), lead (Pb), and mercury (Hg). The physicochemical and heavy metal data were processed and analyzed using Python, and the WQI was calculated to determine the overall groundwater quality classification. The results indicated that all 30 study wells (100%) were classified as unfit for consumption, whereas all six control wells (100%) were classified as excellent based on the WQI classification. The primary contaminants contributing to poor groundwater quality were Ni, Pb, and Hg, which exceeded the applicable national groundwater quality standards at several sampling points. Elevated concentrations of TDS, turbidity, and color were also observed at several study wells. These findings demonstrate a substantial anthropogenic influence of oil exploration activities on groundwater quality in the study area. Continuous groundwater monitoring, improved pollution control, and appropriate environmental management by relevant authorities are therefore essential to minimize contamination and prevent further degradation of groundwater resources.
This study reflects upon the transformability of the chosen principles of Energiewende (Germany’s energy transition) and policy instruments met in the Iraqi energy and urban scenario. The study does not take it for granted that the German model is directly applicable to other countries. Instead, using secondary sources such as policy documents, institutional reports, and academic literature, this study is comparative and context-sensitive. The analysis focuses on six dimensions: technical and network readiness, financial and investment capacity, institutional and regulatory quality, community acceptance and participation, equity in energy access, and renewable energy potential and energy efficiency. Finally, the Multi-Level Perspective is used to analyze and explain how emerging niche innovations, the prevailing socio-technical regime, and the landscape pressures are connected. Results show how elements of the Energiewende can be adapted and are divided into three types: elements which need little adaptation, elements which need significant adaptation, and elements which require more advanced institutional, technical, financial and/or market conditions. Energy efficiency and distributed solar energy, along with smart metering, reduction of transmission and distribution losses, institutional capacity building, and pilot energy storage and microgrids are the most viable components for the Iraqi environment. In contrast, electricity-market liberalization, sophisticated trading mechanisms, large-scale community ownership, and ambitious decarbonization objectives call for a certain amount of contextual provision and deeper readiness of the system. Based on these results, the study proposes three stages of an adaptive pathway: System Stabilization and Enabling Foundations, Expansion and Institutional Embedding, System Integration and Structural Transformation. Movement between stages is determined by observable improvements in readiness and not by pre-determined levels or timescales. The results also showcase how the framework can be implemented at the city, neighborhood, building and infrastructure level linked by smart urban planning, which offers a spatial and institutional context to operationalize the framework. The key enabling measures are: rooftop solar systems, microgrids, energy-efficient urban development, smart metering, demand management based on data analysis, local governance, and spatial-equity safeguards. The study presents an integrated analytical framework that integrates Energiewende principles, readiness assessment, Multi-Level Perspective, and smart urban planning for situations where the performance of the grid is weak, institutions and financial resources are limited, and access to energy service is unequal. Empirical validation of this framework will be needed by engaging the various stakeholders, spatial analysis, techno-economic analysis, pilot activities, and long-term monitoring.
Zero-day attacks–exploiting unknown vulnerabilities before patches exist–pose a critical threat to modern network infrastructure that signature-based intrusion detection systems cannot address. This paper proposes a hybrid computational framework combining a Convolutional Neural Network (CNN)-Gated Recurrent Unit (GRU)-Attention classifier with a skip-connection convolutional autoencoder (AE) for simultaneous known-attack classification and zero-day anomaly detection. The framework introduces three key computational contributions: (1) deterministic reshaping of 64 Random Forest-selected network flow features into 8 $\times$ 8 spatial images, enabling end-to-end CNN processing without feature engineering; (2) a strict Score-based Label Separation and Ordering (SLSO) data partition enforcing complete information isolation between training, validation, and zero-day evaluation sets; and (3) an OR-fusion hybrid decision rule combining anomaly score and reconstruction error signals. Experimental evaluation on Canadian Institute for Cybersecurity Intrusion Detection System (CICIDS)2017 demonstrates 97.48% zero-day detection rate (Z-DR) (95% confidence interval (CI) [97.1%, 97.9%]) at 4.2% false positive rate (FPR) and Area Under the Receiver Operating Characteristic curve (AUROC) of 0.956 across three held-out zero-day attack families–substantially outperforming all classical baselines (best: Stochastic Gradient Descent-optimized One-Class Support Vector Machine (SGD-OCSVM) at 85.45%). SHapley Additive exPlanations (SHAP) explainability analysis reveals mechanistic complementarity: the CNN captures temporal flow signatures while the AE contributes 1,012 exclusive detections via backward inter-arrival time anomalies. The system operates at 14,201 samples/second on Graphics Processing Unit (GPU), satisfying real-time deployment requirements. These results demonstrate that hybrid supervised-unsupervised fusion with rigorous experimental methodology substantially advances zero-day detection capability for computational network security systems.
The rapid expansion of corporate sustainability reporting has substantially increased the volume and complexity of information available to stakeholders, raising fundamental questions about whether additional disclosure continues to generate commensurate informational value. A conceptual framework, termed the Corporate Disclosure Saturation Theory (CDST), is developed to explain how the informational benefits of sustainability disclosure may change as disclosure volume, breadth, and complexity increase. Drawing on a theory-driven synthesis of research on sustainability reporting, integrated reporting (IR), stakeholder information needs, disclosure relevance, measurement, and reporting challenges, the framework proposes that the marginal value of additional disclosure may initially increase as information gaps are reduced but may subsequently diminish once a context-dependent saturation threshold is approached or exceeded. Beyond this threshold, excessive, repetitive, fragmented, or increasingly complex disclosure may impose greater cognitive and interpretive costs on stakeholders, potentially contributing to sustainability reporting fatigue and weakening the decision-usefulness of reported information. The proposed framework further integrates legitimacy theory and stakeholder theory to explain why disclosure may continue to expand even when its marginal informational value declines. From a legitimacy perspective, continued disclosure may be encouraged by institutional expectations, reputational considerations, and pressures to demonstrate organisational accountability, whereas stakeholder theory highlights the importance of aligning disclosure with the information needs, material interests, and decision contexts of diverse stakeholder groups. The framework therefore shifts attention from the quantity of sustainability disclosure towards its informational efficiency, relevance, and usability. The conceptual boundaries and limitations of disclosure saturation and reporting fatigue are also considered, particularly given the absence of a universally observable or measurable saturation point. The CDST provides a basis for future empirical investigation into the conditions under which additional sustainability disclosure ceases to enhance stakeholder decision-making and may instead generate diminishing or negative informational returns. Practical implications are identified for reporting professionals seeking to balance transparency with materiality, comprehensibility, and stakeholder usefulness.
The growing adoption of remote and hybrid work has transformed organizational leadership practices, creating new challenges for trust, communication, and employee engagement. Despite increasing scholarly attention, limited research has examined how employees in high power-distance cultural contexts perceive leadership in remote work environments or how these perceptions contribute to Sustainable Development Goal (SDG) 8: Decent Work and Economic Growth. This study explores how employees in Indonesian organizations interpret leadership behaviors in remote settings and how these interpretations influence psychological safety and employee voice. Using an interpretive qualitative approach grounded in constructivist epistemology, semi-structured interviews were conducted with 48 employees from the technology, financial services, higher education, government, and professional services sectors across Indonesia. Data were analyzed using reflexive thematic analysis (RTA). Four empirical themes were developed: behavioral consistency as the foundation of remote trust; the relational weight of supervisory disclosure; the cultural renegotiation of hierarchy; and the communicative significance of digital micro-behaviors. These themes support three theoretical mechanisms within one integrated model. Digital micro-behaviors provide the signals employees interpret; trust develops through asymmetric accumulation; and cultural permission structures develop partly in parallel, with trust and permission jointly determining whether psychological safety and voice become available. By specifying the conditions under which remote employees can participate meaningfully in organizational decision-making and receive fair and dignified treatment, the findings demonstrate how remote leadership practices can advance SDG 8 through employee participation, psychological safety, inclusive leadership, fairness, and quality of working life. The study offers implications for leadership development, organizational communication design, and inclusive remote-work practices.
Engineering project credit-risk governance requires regulators and project participants to coordinate institutional controls with digital supervision capabilities. Yet decision-makers often lack a structured basis for identifying the factors that should receive priority and for judging how institutional and technological interventions may perform over time. This study investigates the causal structure of engineering project credit risk and examines the policy implications of alternative governance interventions. An online questionnaire collected 86 complete responses covering 33 predefined directional relationships among 13 factors organised under the Technology–Organization–Environment (TOE) framework. Full-precision mean scores were analysed using the Decision-Making Trial and Evaluation Laboratory (DEMATEL), Interpretive Structural Modeling (ISM), and Matrix of Cross-Impact Multiplications Applied to Classification (MICMAC). An exploratory system dynamics (SD) model was then used to compare the baseline, institutional-response, technology, and combined scenarios. Robustness was examined through alternative response coding, threshold sensitivity tests, and 1,000 bootstrap resamples. The results showed that insufficient credit verification by supervision units, environmental and resource compliance risk, and lagging credit-management methods were the three most prominent factors. The ISM analysis placed environmental and resource compliance risk at the root of the four-level hierarchy, while MICMAC classified four factors as independent drivers. All bootstrap samples retained the same three leading factors, and alternative coding preserved the complete prominence ranking (Spearman’s $\rho$ = 1.000). In the exploratory simulation, the technology intervention produced a credit index of 39.09 at time 20, compared with 4.06 under the baseline scenario. The institutional-response intervention showed no clear long-horizon advantage. At time 50, the combined scenario produced a value of 33.73, only slightly higher than the technology-only value of 33.42. These findings indicate that engineering project credit-risk governance should prioritise verifiable supervision, interoperable monitoring, and timely credit-management processes. The integrated framework provides a transparent basis for intervention prioritisation and lifecycle governance, while the simulation results should be interpreted as policy experiments rather than industry forecasts.
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