Low Carbon Emission Research Articles

How Does the Intensive Use of Urban Construction Land Improve Carbon Emission Efficiency?—Evidence from the Panel Data of 30 Provinces in China

China actively explores the green development road by promoting the intensive use of urban construction land and low carbon emissions. This research evaluates urban construction land intensity and carbon emission efficiency by using a multifactor comprehensive evaluation method and SBM model and measures the spatial characteristics of between urban construction land intensity and carbon emission efficiency by Moran’s I and LISA scattering. The result shows that (1) the average value of urban construction land intensity was constantly improving from 0.11 in 2000 to 0.29 in 2021, (2) the value of carbon emission efficiency generally showed a fluctuating downward from 0.89 in 2000 to 0.66 in 2009 and then a fluctuating upward to 0.84 in 2021, with many provinces located in the Level III and IV carbon emission efficiency intervals, (3) the land development, road density, technology development, and fixed asset investment have a negative impact on carbon emission efficiency, while the greening level and tax have a positive promotion effect, and (4) the correlation and spatial features between urban construction land intensity and carbon emission efficiency in 30 provinces have consistency, as well as heterogeneity in the temporal and spatial development trends. Policy implications are accordingly proposed.

Ship-Route Prediction Based on a Long Short-Term Memory Network Using Port-to-Port Trajectory Data

In this study, a ship-route prediction model based on a long short-term memory network using port-to-port trajectory data is presented. Data from a traditional automatic identification system—often used for ship-route prediction—are limited by uneven sampling intervals and noise. To address these issues, equal-interval data collected every 10 s from a target ship, which is a liner container vessel, were employed. Our study focuses on predicting the entire trajectory between the Gunsan and Busan ports. The root mean square error (RMSE), mean absolute error (MAE), and average distance d¯ between two trajectories were used as the key evaluation metrics. The analysis yielded excellent predictive performance, with the values RMSE = 0.000999, MAE = 0.000672, and d¯ = 0.101 km. This study thus provides a foundation for predicting complete port-to-port routes and offers practical insights for managing vessel operations. Accurate route prediction contributes to reducing port congestion, improving fuel efficiency, and lowering carbon emissions.

Carbon meets credit: enhancing corporate ratings using principal component analysis and Gaussian mixture models

This study integrates environmental factors, such as carbon emissions and climate-related risks, with traditional financial metrics to enhance corporate credit rating predictions for approximately 2,000 publicly listed Chinese firms. Utilizing Principal Component Analysis (PCA) for dimensionality reduction and Gaussian Mixture Models (GMM) for probabilistic clustering, the approach effectively addresses multicollinearity and captures nuanced financial-environmental relationships. The PCA-GMM framework not only improves predictive accuracy over traditional methods but also maintains high interpretability, making it suitable for complex risk profiles. Empirical results demonstrate that firms with lower carbon emissions and robust financial health receive higher credit ratings, providing valuable insights for investors and policymakers. This model supports sustainable economic growth in alignment with China’s dual carbon goals by highlighting the critical interplay between financial performance and environmental responsibility.

Impact of Fossil Fuel, Renewable Energy, and Public-Private Partnership Investments on Sustainable Growth and Carbon Emissions in Pakistan

This paper examines the longer-term and causal impact of public-private partnership (PPP) investment in energy and technological innovation on consumption-based carbon emissions in Pakistan during the period from 1990 to 2023. This analysis includes important covariates, such as international trade (exports and imports), economic growth (GDP), and renewable energy consumption, to explore the factors influencing Pakistan's carbon emissions. The theoretical lens for the study is based on the Environmental Kuznets Curve (EKC) theory, which posits that economic growth can initially lead to rising carbon emissions, but stronger long-term technological development and a transition towards renewable energy would turn the tide against environmental degradation. Moreover, Transition Theory emphasizes the importance of PPPs and technological innovation in transitioning Pakistan's energy system from fossil fuels to sustainable energy solutions. By employing advanced estimation techniques, including the Maki cointegration test (1996) that accounts for multiple structural breaks, the research shows that the variables in question are bound in a long-run equilibrium relationship. An export and consumption of renewable energy indicate significantly reduced consumption-based carbon emissions, while more imports, GDP, and (PPP) investment in non- renewable energy positively relates to increased emissions according to results for 182 countries. Overall, the study presents solid empirical proof that trade, technological changes, and a move toward renewable energy can help lower carbon emissions in Pakistan. The authors further recommend the establishment of policies to promote renewable energy investments via PPPs, foster technological progress in low-carbon technologies, implement carbon pricing instruments, such as taxes on energy-intensive goods, and harmonize trade policies to promote low-carbon exports"

Variations in green investment efficiency of enterprises under different low-carbon emission reduction strategies

As environmental issues become more prominent, enterprises increasingly focus on reducing low-carbon emissions through green investment. Simultaneously, governments have implemented various low-carbon emission reduction strategies. This study assesses how varying low-carbon emission reduction strategies influence green investment efficiency in enterprises. The study employed the widely used a slack-based model (SBM) in efficiency estimation to analyze the variations in green investment efficiency under command-based, incentive-based, and public-based strategies. The findings revealed that the coefficient for the command-based strategy was − 0.456, the coefficient for the incentive-based strategy was 0.555, and the coefficient for the public-based strategy was 0.133. All coefficients were statistically significant at the 1% level. The regression analysis results aligned with hypotheses H1-H3, indicating that the command-based strategy hampered green investment efficiency while the incentive-based and public-based strategies enhanced it. These results demonstrate that diverse low-carbon emission reduction strategies yield varying impacts on enterprises’ green investment efficiency. The research results can provide a basis for policy-making in the actual government environmental protection departments.

Experimental, economic, and life cycle carbon footprint assessment of low‐cost adsorbents for siloxane removal from landfill gas

AbstractSiloxane impurities in landfill gas (LFG) pose a significant challenge for downstream processing equipment used in energy recovery. This study investigated the adsorption capacity, cost, and environmental impact of five low‐cost adsorbent materials: biochar, clinoptilolite, hydrochar, diatomaceous‐earth, and crushed glass. Gravimetric analysis was used to determine the adsorption capacity of these materials for octamethylcyclotetrasiloxane (D4). The results were then used to conduct a technoeconomic analysis (TEA) and life cycle assessment (LCA), comparing the selected adsorbents with activated carbon (AC), a commonly used adsorbent for siloxane removal. The TEA revealed that the cost of LFG purification with clinoptilolite, biochar, and AC was $0.035, $0.034, and $0.033/m3, respectively, for the base case studied. The cradle‐to‐gate LCA showed that clinoptilolite had significantly lower carbon emissions compared with the other adsorbents, both per kg of D4 captured and per kg of adsorbent produced. The potential fate of siloxanes after adsorption was discussed, emphasizing the importance of proper treatment and disposal of spent adsorbents. Surface modification techniques were recommended to enhance the adsorption capacity and regeneration of clinoptilolite, potentially reducing cost and carbon emissions in LFG purification. These findings highlight the potential of low‐cost adsorbents for sustainable LFG purification.

Combustion and Emission Characteristics of Methanol–Diesel Dual Fuel Engine at Different Altitudes

Currently, in the two technological approaches for using diesel pilot injection to ignite methanol and partially substituting diesel fuel with methanol, neither can fully achieve carbon neutrality in the context of internal combustion engines. Compression-ignition direct-injection methanol marine engines exhibit significant application potential because of their superior fuel economy and lower carbon emissions. However, the low cetane number of methanol, coupled with its high ignition temperature and latent heat of vaporization, poses challenges, especially amidst increasingly stringent marine emission regulations. It is imperative to comprehensively explore the impacts of the engine geometry, intake boundary conditions, and injection strategies on the engine performance. This paper first investigates the influence of the compression ratio on the engine performance, subsequently analyzes the effects of intake conditions on methanol ignition characteristics, and finally compares the combustion characteristics of the engine under different fuel injection timings. When the compression ratio is set at 13.5, only an injection timing of −30 °CA can initiate methanol compression ignition, but the combustion is not ideal. For compression ratios of 15.5 and 17.5, all the injection timings studied can ignite methanol. Reasonable increases in the intake pressure and intake temperature are beneficial for methanol compression ignition. However, when the intake temperature rises from 400 K to 500 K, a decrease in the thermal efficiency is observed. Particularly, at an injection timing of −30 °CA, both the peak cylinder pressure and peak cylinder temperature are higher, the ignition occurs earlier, the combustion process shifts forward, and the combustion efficiency and indicated thermal efficiency are at higher levels. Furthermore, the overall emissions of NOX, HC, and CO are relatively low. Therefore, selecting an appropriate injection timing is crucial to facilitate the compression ignition and combustion of methanol under low-load conditions.

All-solid-waste cementitious materials for grouting: Effects of alkali content and elemental ratios on performance and sustainability

The use of all-solid waste cementitious materials (ACM) in coal mine grouting backfill offers substantial green, low-carbon, and energy-saving benefits. This study systematically examines the effects of combining carbide slag (CS), fly ash (FA), and ground granulated blast furnace slag (GGBS) on the strength, workability, hydration characteristics, and microstructure of ACM. The utilization of sulfur-containing CS was achieved. The detrimental effects of delayed FA reaction and prolonged setting time can be mitigated by the introduction of high alkalinity or an increase in the Ca/Si ratio. The compressive strength may also be enhanced. The addition of excessive alkalinity (10 %) will result in a prolongation of the setting time. The primary hydration products include calcium aluminum silicate hydrate and magnesium-aluminum layered double hydroxide. Low Ca/Si ratios favor alkali metal ion charge balance, facilitating the transformation of silicate gels from single to double chains, while excessively high ratios reduce polymerization. Gmelinite forms when the (Ca+Na)/(Al+Si) ratio exceeds 1.6, and high NaOH concentrations inhibit ettringite formation. Validation shows that a GGBS:FA:CS= 3:1:3 mix with 4 % alkali binder (Group A2) meets mine grouting backfill criteria, with 80 % lower carbon emissions, 68 % lower energy intensity, and 50 % lower cost than cement. This research offers a viable pathway for the comprehensive utilization of multi-solid waste in mining applications.

Optimal Scheduling of Virtual Power Plants Under a Multiple Energy Sharing Framework Considering Joint Electricity and Carbon Trading

The virtual power plant (VPP) is an excellent approach for mitigating the intermittency and fluctuation of renewable energy sources. The present work proposes an optimal scheduling model for VPPs to leverage the benefits of joint electricity and carbon trading from the perspective of multiple energy-sharing mechanisms. First, the optimal sharing scheduling model of the electric, thermal, and hydrogen energy was established. The model integrates various components, including wind turbines, photovoltaic units, electrolytic cells, combined heat and power units, hydrogen-doped gas boilers, electric energy storage, thermal storage tanks, and hydrogen storage tanks. Then, the model incorporates a tiered carbon trading mechanism to minimize operating and trading costs. Finally, numerical results indicate that, compared with the independent operation of virtual power plants and the lack of joint electricity and carbon trading, the optimal scheduling scheme proposed in this paper reduces the total cost and carbon emissions of the three VPPs by 3.3% and 49.7%, respectively. This demonstrates that the proposed model can effectively reduce the total operating expenses of VPPs by facilitating the allocation of electric, thermal, and hydrogen energy and achieving low-carbon emission operations.

Preface

Good morning, Honorable Governor of West Nusa Tenggara Distinguished guests, speakers, conference participants, and colleagues, I am deeply honored and excited to welcoming all of you to The 3rd International Conference on Mining and Environmental Technology. The theme of our conference this year, “Good Mining Practices and Energy Transition in the Mining Industry,” is highly relevant and significant in today’s global context. Amid the challenges of climate change and the need for sustainable energy sources, the mining industry plays a crucial role. Good mining practices not only ensure environmental sustainability but also enhance operational efficiency and safety. By adopting the latest technologies and best practices, we can minimize the negative impacts on the environment and surrounding communities. The energy transition in the mining industry is also a vital step towards a greener and more sustainable future. The use of renewable energy and innovations in energy technology can reduce our dependence on fossil fuels and lower carbon emissions. It is our collective responsibility to ensure that the mining industry can adapt and contribute positively to global efforts in combating climate change. I am pleased to inform you that there will be 51 presented papers across plenary and parallel sessions. Paper presenters are from New Zealand, Australia, United State of America, Turkey, Malaysia, and Indonesia. In addition, I would like to convey my deepest gratitude to the keynote speakers: Professor Michael Walmsley, Associate Professor Wahidul Biswas, Professor Mahmud Yavuz, Associate Professor Datu Buyung Agusdinata, Associate Professor Hareyani binti Zabidi, and Mr. Tonny Gultom. I hope this conference serves as an effective platform for sharing knowledge, experiences, and innovations in mining and environmental technology. Let us take this opportunity to discuss, collaborate, and find solutions that can be implemented in the field. List of Conference Committees are available in this Pdf.

Guest Editorial: Patience in AI Development Is a Virtue: Why 99% Correct Is 100% Wrong

In heavy-asset industries such as oil and gas, precision is crucial. The saying "99% accurate is 100% wrong" reflects this reality. Despite the excitement of new technology, even minor errors can have significant consequences. For example, an artificial intelligence (AI) system inaccurately predicting a critical machinery component's lifespan could lead to unexpected failures, causing costly downtime and safety hazards. Kongsberg Digital, an early adopter of Microsoft's large language models (LLMs), has witnessed AI's transformative power firsthand. Over the past 2 years, these LLMs have driven a resurgence in AI interest. Generative AI's growth presents unprecedented opportunities for the heavy-asset industry, transforming interactions with complex systems. Implementing AI can optimize maintenance schedules, predict failures before they happen, and streamline operations. The rise of generative AI has also spotlighted the more traditional areas of analytics, classification, prediction, and physics-based simulations. This renewed interest has led the oil and gas industry to look for ways to utilize AI to enhance operational efficiency, reduce costs, and improve safety standards. However, AI inaccuracies or “hallucinations” are deal-breakers. AI-generated misinformation can mislead decision-makers, potentially resulting in disastrous outcomes. In some cases, using AI is unnecessary and adds little value. Heavy-asset industries prioritize safety and have historically been conservative in adopting new technologies. While post-ChatGPT developments are significant, the industry lags due to its zero tolerance for failure. This caution is justified; in environments where lives and substantial investments are at stake, even minor errors can be catastrophic. Moreover, precision in AI ensures not only safety and efficiency but also drives sustainability. Accurate AI predictions minimize waste and reduce energy consumption. Inaccurate decisions can lead to excessive energy use and waste, counteracting sustainability goals. AI can lower carbon emissions and reduce the environmental footprint by optimizing energy usage and ensuring regulatory compliance. Building trust in AI requires responsibility in development and implementation, ensuring security without compromise. We must be transparent about data lineage—a principle that guides our transformative journey. For AI to fully integrate into heavy-asset industries, it must be accurate but also secure and trustworthy. This transparency builds confidence among stakeholders, from engineers to executives.

Sustainable stabilization/solidification of electroplating sludge using a low-carbon ternary cementitious binder

Electroplating sludge (ES), due to its high dosages of heavy metals (HMs), is classified as hazardous waste, presenting risks to both health and environment. Stabilization/solidification (S/S) technology is extensively applied in ES treatment. This study designs a low-carbon ternary cementitious binder, limestone calcined clay cement (LC3), for S/S treatment of ES and investigates the immobilization mechanism of HMs in the crystalline products and calcium alumino-silicate hydrate (C-A-S-H) within LC3. Results show that CrO42- and Cd2+ are primarily stabilized in aluminate crystalline products by replacing SO42- and Ca2+ in the double-layered plate structure, respectively. The remaining CrO42- is captured by the (SiOOH)2AlCa⁺ groups in C-A-S-H generated from substitution of aluminum tetrahedra to bridging silicon tetrahedra. For positively charged Cd2+, it is mainly stabilized through ion exchange with Ca2+ on the C-A-S-H chains. The pozzolanic reaction of calcined clay increases the Al/Ca and Si/Ca atomic ratios in C-A-S-H, enhancing the immobilization efficiency of C-A-S-H to CrO42- and Cd2+. The synergistic effect of calcined clay and limestone, along with the dissolution of aluminosilicates in calcined clay, changes the structure and composition of hydration products, facilitating the chemical binding of CrO42- and Cd2+ by aluminate products. Moreover, the extra hydration products generated by the pozzolanic reaction and the formation of numerous carboaluminate phases strengthen the physical encapsulation performance to HMs. Regarding actual S/S performance, multi-scale evaluation results show that the designed LC3 binder achieves sustainable S/S of ES with high strength, low carbon emission, low cost, and low energy consumption.

Predominance of aminated water interfaces on transition-metal nanoparticulate to enhance synergetic removal of carbonyls and inhibition of CO2 production

In the context of the air quality co-benefits of carbon neutrality, conventional strategies for the end-of-pipe control reduction of volatile organic compounds (VOCs) towards carbon dioxide (CO2) need to be revised more realistically. This study explored the synergetic removal of carbonyls with low carbon emission by amine-functionalized manganese dioxide (MnO2), obtained with a method involving freezing-thawing cycles. Molecular-level characterization revealed that an ordered array of interfacial water dimers (H5O2+, a class of water-proton clusters) on the MnO2 surface enhanced the robust bonding of metal sites with amino groups. Amine-functionalized MnO2 can be negatively charged under environmental acidity to further interfacial proton-coupled electron transfers. Cooperativity in the interfacial chemical processes facilitated the selective conversion of carbonyl carbons to bicarbonated amides (NH3+HCO3−) as a reservoir of CO2. Compared with a commercially used 2,4-dinitrophenylhydrazine (DNPH) control, the nearly complete removal of a priority carbonyl mixture containing formaldehyde, acetaldehyde, and acetone was attained synergically. The secondary organic compounds in the gas phase and CO2 off-gas were suppressed.

Comprehensive assessment of sludge wet air oxidation and its combination with anaerobic digestion

Wet air oxidation (WAO) is a promising sludge treatment technology but has yet to be used widely. This study evaluates a new integrated pathway of AD plus WAO, juxtaposing against the standalone AD and WAO, from the aspects of carbon emissions, energy efficiency, and cost-benefits. The assessment unit is 1 t dewatered sludge with a water content of 80%, and the greenhouse gases are converted to equivalent CO2. WAO can recover the thermal heat released from organic oxidation, resulting in the lowest carbon emissions, 10.92 to −36.86 kg CO2-eq/t. AD plus WAO also performs well with 32.21 to −48.16 kg CO2-eq/t. All three ways can produce renewable energy through biogas utilization or thermal recovery. WAO has the highest energy efficiency of 35%–42% due to efficient thermal energy recovery, and AD plus WAO has a second-ranked efficiency of 15%–30%. However, WAO needs expensive facilities and external electricity, resulting in a higher expenditure than AD. The integrated systems also require a high investment, but can simplify digested sludge treatment. According to the normalized scores in the three aspects, WAO is recommended chiefly, especially for treating low-organic-content sludge. AD plus WAO ranked second and can be used for high-organic-content sludge.

A Bayesian-interference approach to quantify degradation parameters in a water-cooled variable speed screw compressor chiller

Abstract Variable-speed screw chillers can provide effective capacity modulations in commercial buildings to reduce energy consumption, match the load requirements, and contribute to lower equivalent carbon emissions. However, variable-speed operation can also yield to degradation phenomena inside the compressor. Assessing the real-time health status of chillers and detecting anomalies are important aspects to ensure long-term operation and improve reliability. To this end, an automated accelerated life test (ALT) procedure was developed and applied to a 513 kW (145.9 RT) water-cooled variable-speed screw chiller in a laboratory test facility to experimental assess performance degradation over time. After every 1,000 operating hours of ALT, steady-state performance tests at 30%, 50%, 75% and 100% load were conducted according to AHRI Standard 550/590. Additional operating conditions were included while conducting the tests to provide a more complete assessment of the degradation trends compared to the initially measured baseline. The experimental data set was then used to quantify the performance degradation based on the impact on the screw compressor operation. A Bayesian-inference identification approach has been developed to identify changes of sensitive parameters that statistically provide evidence for the likelihood of a performance degradation of the compressor. The information was used to implement the parameters in a dynamic model which was used to perform studies to predict the degradations of a chiller. The outcomes of this study help to improve the maintenance schedule of a compressor and even the whole chiller so that the system can be operated more economically and unplanned downtime can be minimized.

Investigating the impacts of the Dual Carbon Targets on energy and carbon flows in China

As the world’s largest carbon emitter, China has committed to ambitious “Dual Carbon Targets” to address climate change. To investigate the impact of the Dual Carbon Targets on energy consumption and carbon dioxide (CO2) emissions, CO2 emissions were calculated, and Sankey diagrams of energy and CO2 flows for 2018-2022 were drawn based on the latest energy statistics. This study finds that China’s primary energy supply was 5.429 Gtce, with terminal energy consumption at 3.801 Gtce in 2022. CO2 emissions reached 12.01 Gt, marking a 12.24% increase since 2018. Emission intensity varies regionally, being higher in the north and lower in the south, with a national average of 0.1 kg/CNY. Coal continues to dominate energy consumption at 64%, though its share of emissions is declining, particularly in transportation and residential sectors. By 2060, electricity is expected to become the primary energy source, significantly lowering carbon emissions, with Carbon Capture, Utilization, and Storage technologies playing a crucial role in achieving the targets. This analysis provides critical insights into China’s transition to a low-carbon economy, serving as a valuable resource for policymakers to optimize the energy structure and meet environmental goals.

Optimized control strategy for a three-phase grid connected inverter using PI controller and DQ frame

This paper provides a proportional-integral (PI) controller and direct-quadrature (DQ) frame transformation-based optimum control method for a three-phase grid-connected inverter. In terms of grid synchronization, voltage regulation, and harmonic abatement, the proposed control technique attempts to improve the inverter's performance. By separating the control of active and reactive power, the control structure is made simpler and independent regulation of these parameters is possible. This improves the inverter's capacity to quickly react to grid disruptions and track reference values accurately. In order to lower carbon emissions and improve grid dependability, it has become vital to integrate renewable energy sources into the current power grid. Grid-connected inverters are essential in this situation because they transform DC electricity from renewable sources into grid-safe AC power. This abstract outline a proportional-integral (PI) controller and direct-quadrature (DQ) frame-based optimal control method for a three-phase grid-connected inverter using a MATLAB simulation.

Can Big Data Aggregation Help Businesses Save Energy and Reduce Emissions? Quasi-Natural Experiment in Big Data Comprehensive Test

Aggregating big data pieces is critical for increasing enterprise resource allocation efficiency, reducing energy usage, and lowering carbon emissions intensity. This research aims to investigate the impact of big data aggregation on energy efficiency and carbon emission intensity among Chinese enterprises. To this end, it employs primary financial data from Chinese listed companies from 2009 to 2021 and carbon emissions data disclosed in social responsibility reports, sustainable development reports, and environmental reports. The findings revealed that the aggregation of big data elements dramatically reduces the intensity of carbon emissions in firms in the pilot regions. The decrease effect is more effective in economically developed places and regions with higher degrees of digitization, particularly for organizations in high-energy-consuming industries, and it is more robust for small and non-state-owned businesses. The aggregation of big data elements mainly aids firms in pilot regions in lowering energy consumption and emissions by increasing technical innovation and energy usage efficiency. To create a new national competitive advantage, we should actively promote the gradual expansion of the comprehensive pilot zone for big data, advance the in-depth application of big data in environmental governance, and better capitalize on the dividends of big data aggregation.

Coupling Coordination Analysis Between Farmland Transfer and Agricultural Eco-Efficiency in China

The transfer of farmland refers to the process of transferring land contracting and management rights, during which various changes occur in production entities, management behaviors, and management methods. This process fosters positive changes in grain yield and farmers’ income. However, there is no consensus on the ecological impacts of farmland transfer. The objective of this study is to explore the coupling and coordination relationship between the process of farmland transfer and regional agricultural eco-efficiency across 30 provincial units in China. The following conclusions can be drawn from the results: (1) The level of farmland transfer in China exhibits an overall upward trend, primarily influenced by the average farmland area per household in each region and the number of large grain producers, with Heilongjiang consistently maintaining the highest level. (2) The agricultural eco-efficiency value remains relatively stable at approximately 1.1, with high-efficiency areas concentrated in Beijing, Qinghai, Hainan, Shanghai, Fujian, and Guizhou, where lower agricultural carbon emissions are a significant influencing factor. (3) The coupling degree between farmland transfer and agricultural eco-efficiency in various provinces of China is predominantly in a state of adjustment, with the coupling coordination level improving year by year. However, the coupling coordination level does not demonstrate a clear “high-high” or “low-low” synchronous state with grain yield per unit area, indicating that the levels of farmland transfer and agricultural eco-efficiency have not yet established a robust interactive relationship with farmland production efficiency.

Techno-economic analysis of green hydrogen production from wind and solar along CPEC special economic zones in Pakistan

Hydrogen-based systems are garnering attention as part of efforts to achieve low-carbon emissions and net-zero targets. In developing countries, natural gas and coal serve as a primary source for hydrogen production, given their accessibility and cost-effectiveness. Green hydrogen presents a promising clean energy solution with significant potential to decarbonize the industrial sector. In this article, a techno-economic analysis has been performed for green hydrogen production using wind and solar as primary energy sources. The analysis is conducted at nine special economic zones (SEZs) and a free zone at Gwadar Sea Port using the Hybrid Optimization of Multiple Energy Resources (HOMER) Pro software. A hybrid energy system has been designed to meet the industrial electrical and hydrogen demand of 600 MWh/day and 60 tonnes H2 per day, respectively. A comparative analysis of on-grid and off-grid systems across all SEZs is performed for the optimal system. A sensitivity analysis is conducted on different parameters that could impact the levelized cost of hydrogen (LCOH). The results indicate that LCOH varies from 4.19 $/kg to 8.66 $/kg for off-grid and 2.12 $/kg to 4.65 $/kg for on-grid systems which is a competitive cost to other countries. The most feasible economic zones for green hydrogen production are found to be Dhabeji and Port Qasim with the lowest LCOH of 4.19 $/kg and 4.22 $/kg for off-grid, 2.12 $/kg and 2.36 $/kg for the grid-connected system, respectively. Dhabeji also exhibits the lowest CO2 emissions per year making itself the most feasible location for green hydrogen production. Grid-connected systems are a great opportunity for Pakistan to produce low-cost green hydrogen for industrial decarbonization and the country’s economic growth.