Future Sustainability

Regulatory and standard insights on transboundary CO2 in the context of MRV

2025-05-16T14:57:32+00:00

As transboundary carbon capture, utilisation, and storage (CCUS) projects gain momentum globally, the need for a coherent, robust, and verifiable system for measurement, reporting, and verification (MRV) of cross-border carbon dioxide (CO₂) flows becomes increasingly critical. This paper reviews and synthesises key regulatory frameworks and technical standards, namely, the EU Emissions Trading System (EU ETS), ISO 27914 and ISO 27915, and the Verra VM0049 methodology, to assess their applicability to MRV across the CO₂ capture, transport, and storage chain. The EU ETS, under its 2024 consolidated Implementing Regulation, sets a high benchmark for uncertainty management and data integrity in CO₂ accounting; however, it lacks specific prescriptions for advanced or smart metering technologies. ISO 27914, while focused on geological storage, provides essential guidance for long-term containment and injection site monitoring, relevant to the final stages of the CCS chain. ISO 27915 provides a comprehensive framework for quantifying and verifying GHG emissions and reductions, establishing a direct link between CO₂ flow measurement and emissions reporting. The Verra VM0049 methodology, although designed for voluntary carbon markets, provides comprehensive procedures for quantifying and monitoring emissions across transport and storage stages, with practical relevance to transboundary CO₂ transfers. While none of these instruments independently address all aspects of cross-border CO₂ movement, their combined insights highlight both foundational strengths and critical gaps, such as the absence of unified custody transfer protocols and limited treatment of fugitive emissions in transitional zones. This paper aims to consolidate these insights to inform future MRV frameworks tailored to the unique technical, regulatory, and jurisdictional challenges of transboundary CO₂ flows.

Evaluating the impact of economic policies on solar energy growth in Iran

2025-06-02T22:05:54+00:00

This paper explores the techno-economic implications of Iranian policy instruments designed to promote large-scale photovoltaic (PV) power plants. As global energy demands rise and environmental concerns intensify, transitioning from conventional fossil fuels to renewable energy sources has become imperative. This study investigates the current state of Iran's electricity market and the effectiveness of its power purchase policies in facilitating PV development. Despite possessing substantial solar energy potential, Iran faces significant challenges, including financial constraints and inconsistent energy policies, which hinder the swift adoption of renewable technologies. The research utilizes a comprehensive approach to assess these barriers and proposes strategic financial solutions to enhance investor confidence and participation in the solar energy sector. Notably, this study contributes to the existing literature by providing a detailed analysis of Iran's unique socio-economic context and its impact on the implementation of renewable energy policy. The findings underscore the necessity for cohesive governmental support and innovative financing mechanisms to unlock Iran's vast solar resources, ultimately paving the way for sustainable energy solutions that align with global carbon neutrality goals.

Analysis of fatigue of construction workers based on electromyographic signals

2025-06-25T07:28:42+00:00

Aiming to address the fatigue issue of construction workers resulting from high-intensity physical labor, this paper proposes a fatigue analysis method based on surface electromyographic signals (sEMG), focusing on the handling operation as the research object, to explore the fatigue characteristics of construction workers' muscles and significant monitoring indices. By collecting sEMG signals under different fatigue levels, we analyze the trends of time-frequency domain indicators (root mean square value RMS, integral EMG value IEMG, median frequency MF, mean power frequency MPF, and over-zero rate ZCR). The experimental results show that with the increase of fatigue, the RMS and IEMG of brachioradialis and erector spinae increase significantly, while the MF and MPF decrease significantly, which reflects the physiological mechanism of the decrease of muscle contraction efficiency and the enhancement of neural drive. The changes in the indexes of erector spinae are more significant than those of brachioradialis due to the higher stability load and the activation characteristics of fast muscle fibers. Through the test of intergroup variability, RMS, IEMG, MF, and MPF are selected as the core indicators for fatigue monitoring. This study provides an objective, quantitative basis for labor protection in the construction industry and lays a theoretical foundation for the real-time monitoring of occupational fatigue and the optimization of work efficiency.

Regression analysis and classification of temperature modulated metal oxide semiconductor gas sensors responses on flue gas

2025-07-19T22:54:59+00:00

Industrial emissions, particularly from flue gases, pose significant risks to environmental sustainability and public health. Conventional air quality monitoring systems often suffer from high costs, delayed reporting, and limited detection capabilities. This study presents a cost-effective, real-time air quality monitoring solution using an electronic nose (eNose) system integrated with Metal Oxide Semiconductor (MOS) gas sensors. These sensors target key pollutants, such as carbon monoxide (CO) and carbon dioxide (CO₂), which also serve as indicators of transformer faults in industrial settings. The eNose system leverages machine learning for both regression and classification tasks, enabling accurate quantification of pollutant levels and categorization of air quality into defined categories. Principal Component Analysis (PCA) is employed to optimize feature extraction, enhancing model precision and efficiency. Notably, the system integrates digitally controlled buck converters for automatic temperature regulation, reducing sampling time from 390 to 130 seconds. Additionally, a redesigned airtight sensor chamber and optimized airflow design, along with the use of Tedlar bags, improve sample integrity and minimize interference. Hardware development involved prototyping on breadboards using LM2575, LM2576, and LM2574 ICs, followed by the creation of a compact 10 cm × 10 cm PCB for efficient power management. Multimeter testing verified reliable electrical connections. Experimental validation showed the system achieved over 91% accuracy in distinguishing between "good" and "bad" air quality levels. Strong correlations between sensor output and pollutant concentrations confirm system reliability. This research demonstrates a scalable, efficient tool for real-time air quality monitoring and fault detection in industrial environments.

Examining the association between deteriorated urban fabric and socio-economic resilience in Tehran Metropolis

2025-07-25T14:07:09+00:00

Deteriorated urban areas usually face social, economic, and environmental problems. They often struggle with issues like poverty, inadequate housing, poor public spaces, social isolation and a sense of hopelessness, limited business opportunities, and a lack of investment. These complex problems cause significant disaster resilience challenges for these areas. This article investigates the association between urban deteriorated fabric (UDF) rate and socioeconomic resilience (SER) in the neighborhoods of Tehran Metropolis. Fourteen SER variables are identified through a literature review. Exploratory factor analysis is used to transform them into fewer factors. Four factors are extracted and are labelled as economic, social, economic-demographic, and community capital resilience. Similar extracted factors are combined to obtain social and economic resilience subcomponents. Jenks' Natural Break classification method is used to classify the UDF rate into five categories. Ordinary least squares (OLS) and Geographically Weighted Regression (GWR) are used to examine the association between UDF rate (dependent variable) and SER subcomponents (independent variables). The findings of the study show that: (a) the GWR better captures spatial relationships between UDF rate and SER factors than the OLS method, (b) the relationship between DUFs and social and economic resilience is complex and not definitively one-sided, and (c) social and economic resilience can occur concurrently in DUFs, (d) neighborhoods with high UFD rates are clustered in the mid-southern parts of the Tehran city. Understanding the interplay between social and economic resilience in DUFs is crucial for developing effective strategies to promote recovery and long-term disaster resilience and sustainability.