Green Industry Research Articles

Multilevel modelling of sustainable consumption for achieving carbon neutral production – case study of the urban in Thailand

The research aims to determine a living model that is eco-environmental and sustainable. Climate change and scarcity of resources have led world policymakers, along with the United Nations, to set guidelines in the form of 17 sustainable development goals (SDGs). Sustainable production consumption is one of the major issues that is considered worldwide, including the SDG mission of the University ranking under SDG12.The paper proposes a new model development for sustainable consumption production using multi-criteria decision-making of the Fuzzy Analytical Hierarchy Process (FAHP) under the 5P principle. The questionnaire was designed and distributed to sample populations in the community, and the analysis was done under the FAHP procedure. The research area focused on green space near Bangkok, Bang Kachao. It is one of six local governmental units (Tambon) in Phra Pradaeng district located in Samut Prakran province, Thailand.It was found out that people concerned the most sustainable production consumption using natural local materials at 25.09%, followed by making community products by green industry at 12.42% and making local green products at 6.18%. From the development of the multi-modelling framework, the paper proposes a new model of the urban community in Thailand for sustainable production consumption to support SDG12 using FAHP for multi-decision making based on the 5P principle. There are people, porosity, planet, peace, and partnership.However, various factors influence production and consumption and impact the carbon footprint.It was obviously found that people mostly use local materials to make green local products under the local policy of 3R waste management. Innovative design uses community wisdom, knowledge and know-how to make value-added products.The strategic planning and control consist of green industry, zero waste management, zero carbon footprint and innovative product design. The expected outputs from the model are green and homemade products with cleaner production, clean energy, and gain carbon credit.

Optimization method for capacity configuration and power allocation of electrolyzer array in off-grid integrated energy system

Hydrogen production using renewable energy is in line with China's the goal of carbon peak and carbon neutrality. The construction of off-grid hydrogen energy industrial park can effectively achieve local utilization of renewable energy and develop the green hydrogen industry. However, off-grid hydrogen energy industrial park has not the compensation and sustentation from large power grid, the stable operation and economic production of electrolyzer are affected by significant factors, such as power fluctuation and frequent start stop. Therefore, this paper proposed the optimization method for capacity configuration and power allocation of electrolyzer array in off-grid integrated energy system. Firstly, based on units of energy supply, energy conversion, and energy storage, a structural model of off-grid integrated energy system was established. Then, by analyzing the operational characteristics of single electrolyzer and electrolyzer array, flexible mode of multiple electrolyzers operating in combination was proposed. Furthermore, considering the hydrogen production cost, the mean of hydrogen production volatility and penalty cost, the capacity configuration and power allocation methods for electrolyzer array were proposed, and the optimization problem is solved with multi-objective particle swarm optimization algorithm. Finally, the hydrogen energy industrial park in Zhangye is taken as example to verify the effectiveness of above research content. Compared to a single type of alkaline electrolyzer, the mean of hydrogen production volatility of multiple electrolyzers operating in combination has decreased by 43.2 %, the hydrogen production quantity has increased by 9.74 %. Compared to a single type of proton exchange membrane electrolyzer, the hydrogen production cost has been reduced by 14.16 %. Compared with the balanced running mode, the flexible operation mode can improve the cost-effective and quantity of hydrogen production.

Psychological factors leveraging success to SMEs in Indian green automobile industry

The current work draws comparisons with stimulus–organism–response (SOR) theory and provides a pertinent levering comprehension for small and medium enterprises (SMEs) in the Indian automobile industry, by investigating consumers’ attitude towards electric vehicles (EVs) offered by them. Four factors, (1) ostentatious (conspicuous) consumption, (2) ecological behaviour, (3) utilitarian shopping values and (4) lifestyle, have been accounted for, in this regard. Additionally, the study looks at how this mindset affects Indian customers’ intentions to consistently use EVs (offered by SMEs). A sample of 382 Indian owners of EVs provided information via a standardized questionnaire. To make conclusions from the obtained data, the structural equation modelling (SEM) approach was applied. According to the findings, only utilitarian shopping value – one of the four antecedents – has no discernible correlation with attitudes towards EVs that Indian SMEs sell. Also, the study provides evidence that attitude positively relates with continuous intention to use EVs.

Social Media Usage Among Green Industry Firms

Social Media Usage Among Green Industry Firms

The influence of regional industrial structure on the suitability of biomass energy development: A case study of 30 counties and cities in a cold region of China

The development of the biomass economy contributes to economic growth and social development. The study of biomass energy economy from the perspective of industrial structure type has the significance of promoting the optimization and upgrading of industrial structure, promoting the development of green industry and other aspects. It helps to promote the sustainable development of the economy and society. And industrial structure is a major component of the socio-economic system. Therefore, it is necessary to study the impact of material energy supply and demand from the perspective of regional industrial structure type. The objective of this study is to explore the relationship between the type of industrial structure and the development of biomass energy from the perspective of the economic direction, so as to fill the gap in this research perspective. Based on the three major industries generally regarded to constitute GDP - agriculture, industry, and service - the regression results of STATA software were used to calculate biomass potential according to crop distribution status. Using the ArcGIS analysis tool, the biomass break-even model related to resource supply-demand has been used to obtain the break-even status of the research area under six industrial structures in the past 20 years. According to research results, the break-even degree of biomass energy supply is between 0.109 and 1.236 when the industrial structure types of counties and cities are service-agriculture-industry and industry-agriculture-service. This is suitable for biomass energy development. When the industrial structure type of counties and cities is service-industry-agriculture, the break-even degree of biomass energy supply is between -0.428 and 0.277, which is not suitable for biomass energy development.

Model for Strengthening MSMEs in the Cultural Heritage of Borobudur Temple Based on the Green Industry Concept: A Mixed-Method Analysis

Model for Strengthening MSMEs in the Cultural Heritage of Borobudur Temple Based on the Green Industry Concept: A Mixed-Method Analysis

Fine-tuning N-doped species of C catalysts for 98% current efficiency of electrocatalytic decarboxylation into hindered ether

Regulating the interaction between the substrate and electrode is crucial for maximizing catalytic performance. In this study, we developed a method for controlling the sintering temperature and introducing H2O2 post-treatment to modulate the N-doping of C catalysts, enhancing the interaction between the substrate and electrode to cause a radical reaction, thereby promoting electrocatalytic decarboxylation. When 10 g of feedstock was used, the electrocatalytic system exhibited 9.4- and 4.2-fold increases in productivity and current efficiency, respectively, compared with the conventional method. A systematic investigation combining experiments and theoretical calculations revealed that pyridine N-oxide units not only promote bridging adsorption of the substrate and the formation of a substrate-enriched electric layer but also the transfer of mass and electrons, generating more reactive carboxyl radicals. The electrocatalytic system delivers a current efficiency of 98%, which is exceptional compared to previously reported electrocatalysts. The system is in line with the development trend of the green chemical industry, combining flow reactors and photovoltaic technology. This study offers valuable insights and guidance for advancing electrocatalytic organic synthesis for future industrial applications.

A Novel Efficient and Biodegradable Natural Catalyst and Bio Based Solvents for the Green One Pot Three Component Synthesis of Tetrahydrobenzo[B]pyran and 3,4-Dihydropyrano[C]chromenes

This study presents a novel and efficient method for synthesizing Tetrahydrobenzo[b]pyran and 3,4-Dihydropyrano[c]chromene derivatives using honey as a highly efficient homogeneous catalyst. Through a one-pot three-component condensation of aromatic aldehydes with malononitrile and dimedone or 4-hydroxycoumarin, excellent yields were achieved under environmentally friendly conditions. The utilization of honey, a non-toxic and biodegradable catalyst, underscores the method's significance in green chemistry. The abstract highlights the method's advantages, including high yields, short reaction times, simple work-up, and its potential for applications in the green pharmaceutical and chemical industries. Additionally, the intricate role of honey as a catalyst in chemical reactions is explored, shedding light on its mechanism and potential applications in organic synthesis and other chemical processes. This research introduces a promising and environmentally friendly approach for the green synthesis of biologically active heterocyclic compounds using a natural, biodegradable catalyst, contributing significantly to the advancement of green chemistry practices.

Artificial intelligence enabled self-powered wireless sensing for smart industry

Traditional batteries or external supply powered wireless sensing system are needed to be improved for realizing the development of the smart industry with low-carbon, green and sustainable. This paper proposes and develops a self-powered wireless sensing system for smart industry (SPOT), utilizing a triboelectric nanogenerator (TENG) coupled with an artificial intelligence (AI) transformer model. The SPOT system includes the TENG-based self-powered flexible sensor (SWNG), the wireless aggregate node (WAN), the electromagnetic and TENG hybrid generator (ETCG), and the monitoring and management center with an AI model (MACA). The ETCG serves as a power source for the WAN. The SWNG acquires voltage signals from products on the conveyor belt in the smart industry, powered by the TENG, and transmits the sensor data wirelessly to the MACA via the WAN for processing. The MACA processes the data using the transformer AI model, which not only ensures self-sustainability and long-term stability but also enables intelligent recognition and monitoring of industrial products by their packaging materials, thereby providing precise status information and decision support for the smart industry. The transformer model’s deployment in the MACA has demonstrated robustness and a high classification success rate of up to 97.8 %, efficiently categorizing multiple targets. Additionally, the SWNG and WAN exhibit low power consumption of approximately 80 mW, successfully contributing to the realization of green, low-carbon objectives. The SPOT system significantly enhances the efficiency of product transportation and management within the smart industry and contributes to the advancement of a sustainable, low-carbon, and green smart industry, offering novel technological insights and pathways for future development.

Analyzing the Implementation of Green Industries Practice at PT. Semen Indonesia (Persero) Tbk. Tuban Plant towards Achieving Sustainable Development Goals

This research examines the implementation of green industry practices at PT Semen Indonesia (Persero) Tbk Tuban Plant and its contribution to the achievement of Sustainable Development Goals (SDGs). As a state-owned multinational corporation in the cement industry, PT Semen Indonesia (Persero) Tbk Tuban Plant plays a significant role in integrating environmental sustainability into its business operations. Through a qualitative exploratory approach, data was collected through in-depth interviews, observation, and documentation study. The analysis revealed that the company has implemented various green industry practices across its operations, including the adoption of Alternative Fuel and Raw Material (AFR), Waste Heat Recovery Power Generation (WHRPG) technology, and community development programs. These practices align with several SDGs, particularly SDGs 7, 9, 11, 12, 13, 14, and 15, focusing on clean energy, innovation, sustainable cities, responsible consumption and production, climate action, life below water, and life on land. The research highlights PT Semen Indonesia (Persero) Tbk Tuban Plant as a pioneering entity in environmental management and community empowerment within the cement industry, contributing significantly to sustainable development efforts.

Optimization of Extraction Solvents on the Antioxidant Properties of Coconut Waste

Cocos nucifera L. (family Arecaceae) commonly known as coconut is considered as an important fruit crop in tropical countries and are widely used for therapeutic and domestic purpose. They have effective properties such as antioxidant, antitumor, antiseptic and antimicrobial. The growing demand for green coconut water consumption and food industries cause the dumping of the shell and husk of this fruit, generating large amount of solid waste. This study utilized part of coconut waste to study the impacts of solvent type on the phenolic content. Six different polarities of solvent were chosen. Total phenolic content was performed using Folin-Ciocalteu assay and free radical scavenging activity with 2,2-diphenyl-1-picrilhydrazil (DPPH) method. Total phenolic content for coconut shell was the highest at 71.57 ± 0.275 mg GAE/g for propanol extract and 74.10 ± 0.741 mg GAE/g for acetone extract of coconut husk. The antioxidant activity of all sample extracts was analysed using DPPH assay. Highest radical scavenging activity of coconut husk was observed by propanol extract with 93.82 ± 0.052 % while the lowest scavenging activity was demonstrated by chloroform extract with percentage of 57.77 ± 2.255%. Acetone extract of the shell exhibited the highest scavenging activity of 91.23 ± 0.073%, while chloroform extract of coconut shell demonstrated the lowest antioxidant activity of 70.627 ± 0.467%.

Nexus among artificial intelligence implementation, healthcare social innovation, and green image of hospitals’ operations management in Egypt

The aim of this paper is to decipher and investigate the relationship between artificial intelligence implementation (AII), healthcare social innovation (HSI) and hospitals’ green image (HGI) in an Egyptian emerging market. An interdisciplinary research with a mixed-methods approach was conducted to add bricks, conceptually and empirically, that fill a literature gap between using this evolving AI-technology and sustaining socially-innovative medical operations and supply-chain management (OSCM) practices in a dynamic green healthcare industry. After scanning the relevant transdisciplinary literature, the authors developed and tested a conceptual model through analysing data collected from 116 quantitative questionnaires answered by healthcare managers/leaders in 46 Egyptian hospitals. After applying structural equation modeling using SmartPLS-v4, the results illustrated that AII has a positive significant impact on HSI, which has a positive significant influence on HGI. Furthermore, AII-HGI relationship can be fully-mediated significantly by HSI, which confirms the significant role of AI-powered technology in reinforcing socially-innovative medical OSCM processes to sustain an environmental-friendly image of Egyptian hospitals. Based on these quantitative findings and qualitative fruitful interviews with healthcare leaders and technology experts, this article used a comprehensive approach to contribute to AI-enhanced healthcare OSCM literature in interdependent ways. It encapsulated the benefits and obstacles of using AI-driven socially-innovative green medical OSCM practices in emerging healthcare markets similar to Egypt. Moreover, it proposed a balanced scorecard map for communicating and evaluating socially-responsible ecofriendly OSCM strategy of AI-powered hospitals. As for the practical implications, the medical operations and supply-chain managers of similar emerging healthcare markets can benefit from the exemplars highlighted throughout this paper on how their hospitals can invest in AI-based healthcare processes/services, which can provide creative and scientific solutions for social, educational, environmental and economic problems within their countries. Thus, reflecting on the social implications, these digitally-transformed hospitals can contribute innovatively to the social wellbeing of their communities and promote their green image among their supply-chain stakeholders.

Dehydration of lactic acid to prepare acrylic acid over Hβ zeolite modified by acid treatment and Rb ion-exchange

The catalytic dehydration of bio-derived lactic acid into acrylic acid is one of the research hotspots in the field of green chemical industry, but the selectivity of acrylic acid and the stability of catalyst still face great challenges. In this work, a modified Hβ zeolite catalyst was prepared by sequential nitric acid treatment and Rb ion-exchange for the gas phase dehydration of lactic acid to prepare acrylic acid. The nitric acid treatment increased the content of Lewis acid sites together with the decrease of the content of Bronsted acid sites, which enhanced the dehydration and hindered the decarboxylation/decarbonylation of lactic acid. Consequently, the selectivity of acrylic acid was improved. The Rb ion-exchange not only significantly decreased the density of acidic sites but also regulated the density of basic sites of the catalyst. The optimal 0.1NA-6.8Rb/Hβ catalyst treated by 0.1 mol/L of nitric acid and 6.8 wt% of Rb ion-exchange contributed to a 99.28 % conversion of lactic acid and 76.72 % yield of acrylic acid. The catalyst showed a better stability and was easy to regenerate, maintaining 87.35 % conversion of lactic acid and 62.49 % yield of acrylic acid, respectively, after two cycles of consecutive 48 h reaction.

Fabrication of Monodisperse Magnetic Polystyrene Mesoporous Composite Microspheres for High-Efficiency Selective Adsorption and Rapid Separation of Cationic Dyes in Textile Industry Wastewater.

Sustainable development has become an inevitable trend in the world's green chemical industry for a generation or more. In this study, a monodisperse magnetic polystyrene mesoporous composite microsphere (MPPS) composed of Fe3O4 nanoparticles loaded on polystyrene mesoporous microspheres is introduced. These microspheres serve as effective adsorbents for the swift removal of cationic dyes. The fabrication of the wastewater adsorbent, with its simple operation and economic practicality, involved a combination of dispersion polymerization, a sulfonation reaction, two-step swelling polymerization, and in situ alkaline oxidation technology. Notably, the adsorption capacity within 3 min reaches 184.0 mg/g, with an impressive adsorption efficiency of 92%. This is primarily attributed to the high specific surface area (Smax) of the MPPS providing more reaction sites for π-π interaction. Simultaneously, the attractive force between negatively charged sulfonic acid groups and cationic dyes is enhanced through surface modification of the MPPS. Furthermore, the MPPS, boasting a maximum saturation magnetization of 38.19 emu/g, ensures rapid separation from the solution for recycling within 3 s. Even after 5 cycles, the adsorption efficiency remains over 90%. The rapid separation of dyes is facilitated by the magnetic attraction of Fe3O4 nanoparticles from the MPPS under the application of a magnetic field. These composite mesoporous materials exhibit outstanding performance in both efficient selective adsorption and recyclability, presenting a novel green adsorbent with promising prospects for sustainable development. This innovation is poised to excel in fields such as sewage treatment, separation, and purification.

Green hydrogen prospects in Peninsular Malaysia: a techno-economic analysis via Monte Carlo simulations

According to Malaysia's National Energy Transition Roadmap, hydrogen is a critical component of the country's energy transition. However, there is a scarcity of hydrogen studies for Peninsular Malaysian states, which limits discussions on green hydrogen production. This study employs a Monte Carlo model to assess the economic and technical factors influencing the success of green hydrogen in Peninsular Malaysia. The study focuses on three target years: 2023, 2030, and 2050, representing various stages of technological development and market adoption. The levelized cost of hydrogen (LCOH) of a 1-MW Proton Exchange Membrane (PEM) electrolyzer system ranges from $5.39 to $10.97 per kg in 2023, highlighting early-stage challenges and uncertainties. A 6-MW PEM electrolyzer system could achieve an LCOH of $3.50 to $4.72 per kg by 2030, indicating better prospects. Because of technological advancements and cost reductions, a 20-MW PEM electrolyzer system could achieve an LCOH of $3.12 to $3.64 per kg in 2050. The findings indicate that the northern regions of Peninsular Malaysia have consistently low LCOH values due to favorable geographical conditions. Due to minor variations in solar capacity factors, uncertainty distributions in LCOH remain stable across different regions. Some states may face increased uncertainty, emphasizing the need for additional policy support mechanisms to mitigate risks associated with green hydrogen investments. The sensitivity analysis shows that key cost drivers are shifting, with early-stage electrolyzer investments dominating in 2023 and electricity prices becoming more important in 2030 and 2050. Future research could focus on optimizing green hydrogen systems for areas with underdeveloped green hydrogen industries. This study contributes to informed discussions about green hydrogen production by emphasizing the importance of tailored strategies that consider local conditions and highlighting the role of Peninsular Malaysia in the energy transition.

A pathway to urban resilience: investigating green open space priority areas to mitigate the urban heat island phenomenon in Samarinda City, Indonesia

PurposeThe potential for massive economic growth exists in Samarinda City due to the intensification of activities in built-up areas. This suggests the potential for increased urban disease in the relocation of Indonesia’s new capital city to a location adjacent to Samarinda. One of the most striking impacts is the urban heat island (UHI). The increase in this phenomenon can be addressed effectively and efficiently through the provision and arrangement of appropriate vegetation-based actions. Therefore, this study aims to identify priority areas of green open space (GOS) based on UHI levels. In addition, this study also aims to present alternative mitigation measures to reduce the risk of disasters due to UHI.Design/methodology/approachA mixed-method approach was used in this research, involving an initial land surface temperature analysis to identify the UHI class. This analysis was complemented by quantitative spatial analyses, such as scoring, overlay and intersect methods, to determine the priority level class and the typology of GOS priority. A qualitative analysis was also conducted through data triangulation or comparison methods, such as examining existing land use, GOS priority maps and spatial plan policies.FindingsThe findings show that the total UHI area in Samarinda City was 6,936.4 ha in 2019 and is divided into three classifications. In Class 1, the UHI area is very dominant, reaching 87% of the total area. Meanwhile, the main results identified two priority classes of GOS in Samarinda, namely, the medium and high categories with an area of 960.43 ha and 113.57 ha, respectively. The results also showed that there were 17 typologies associated with five alternative mitigation measures: green industry, greening parking lots, improving urban green infrastructure and buildings, urban greening and mining restoration.Research limitations/implicationsSpecific to assessing UHI, image data were available only in medium spatial resolution, leading to a consequence of detailed accuracy. In addition, since the determination of mitigation considered local policies, the method should be used in other locations requiring adjustments to existing regulations, specifically those related to spatial planning.Originality/valueThis study makes a significant contribution to the understanding of the UHI phenomenon in Indonesia, especially in the urban areas of Kalimantan Island. In addition, the study presents new insights into alternative mitigation actions to reduce the risk of UHI. Innovatively, this study introduces a typology of regions associated with appropriate alternative mitigation actions, making it an important achievement for the first time in the context of this study.

Research on Stakeholder Behavior Strategy in Green PPP Project Financing

In the context of green development, while promoting green industry and sustainable development, green PPP faces the challenge of financing difficulties. Based on the evolutionary game theory of bounded rationality and taking government, social capital and financial institutions as the game players, this paper constructs an evolutionary game model for the behavioral decision-making of stakeholders in green PPP project financing. Using MATLAB software, we simulate and analyze the key factors affecting the behavioral strategies of each game player. The results indicate that: (1) The additional income obtained from participation in green PPP projects is a key factor affecting the decision-making of social capital and financial institutions, so it is necessary to improve the reasonable return mechanism of green PPP projects and attach importance to the construction of reputation mechanism; (2) Government subsidies can effectively encourage social capital and financial institutions to participate in green PPP projects, but the amount of subsidies should be within a reasonable range; (3) The government's reward and punishment intensity is a significant factor affecting the decision-making of social capital behavior. To this end, it is imperative for the government to enhance the performance evaluation system for green PPP projects and establish a balanced reward and punishment mechanism. This paper puts forward some suggestions on the decision-making of stakeholders in the financing process of green PPP projects, and provides references for the policy formulation of encouraging social capital and financial institutions to participate in green PPP projects.

Examining nonlinear effects of socioecological drivers on urban solar energy development in China using machine learning and high-dimensional data

In the context of carbon neutrality target, renewable energy sources have been transforming from “supplementary energy” to “main energy”, which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the “resource-environment-demand” comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry.

The Lac à l'Orignal phosphate deposit and constraints on high-quality phosphatic ore in massif-type anorthosite, Grenville Province, Canada

Phosphate is an indispensable component in the fertilizer industry. It has recently become a critical part of the green energy transition because of the demand for lithium ferro phosphate in rechargeable batteries. A significant amount of phosphate ore (∼90 %) is globally produced from sedimentary rocks. High-quality phosphate ore containing low amounts of toxic elements, however, is mostly produced from igneous carbonatite.The Lac à l'Orignal deposit (∼1069–993 Ma), Canada, in the central Grenville Province, contains a large amount of fluorapatite (≤ 20 %) hosted in oxide-bearing norite-gabbronorite-anorthosite layered intrusions. The deposit is hosted in the Vanel Anorthosite (1080 ± 2 Ma) near the northern border of the Mattawa Anorthosite (1016 ± 2 Ma). The major host rock oxide-apatite-gabbronorite (OAGN) is primarily composed of plagioclase, orthopyroxene, clinopyroxene, fluorapatite, ilmenite, and magnetite. The Al contents of the OAGN orthopyroxene indicate that the magmatic deposit was emplaced at mid-low crustal levels (∼2.0–3.6 kbar) and reverse zoning in the OAGN plagioclase suggests <2.5 kbar of pressure change during their crystallization. An overall increase of REE + Y concentrations in the OAGN fluorapatite with decreasing their fluorapatite contents indicates that the magma went through fractional crystallization. This study suggests that high abundances (ca. > 8 %) of fluorapatite in some OAGN were produced from crystal avalanching after initial crystal settling. Antithetic correlation between the Cl content of fluorapatite and the Mg# [Mg/(Mg + Fe2+)] of coexisting orthopyroxene suggests that phosphate ores with low Cl contents are expected for OAGN that formed at the initial stages of magma crystallization. Therefore, the Mg# of the OAGN orthopyroxene provides an exploration tool for targeting phosphate ore with low Cl content, which is preferred in the production of phosphoric acid, a major intermediate feedstock for the fertilizer and green energy industries. Apatite contents in carbonatite and average P2O5 content in carbonatite melt are generally similar to those of the Lac à l'Orignal OAGN. The average concentrations of REE, and toxic metals, such as Pb, Th, and U, in the OAGN fluorapatite are, however, lower than those in apatite from carbonatite worldwide. Therefore, the apatite of the OAGN appears to be of high quality and more environmentally-friendly as a source of phosphate ore.

What Attracts Green: Value Differentiation of Environmental Industry and Coagglomeration with Pollution-Intensive Industries in China

Scholars have spent decades studying how environmental (“green”) industry can promote sustainability. Few studies, however, have discussed corporate dynamics within green industry and their spatial interactions with other manufacturing and service industries. Drawing on the theoretical perspective of industrial value chain differentiation, this study unpacks the spatiotemporal dynamics of environmental enterprises by various types in China. We use the Wasserstein coagglomeration index and Poisson regression to identify the spatial coagglomeration of green and pollution-intensive (“brown”) industries. The results show that (1) China’s environmental industry is still in the early and middle stages of development with low and medium added value as the core; (2) green enterprises are mainly distributed in the eastern coastal areas and a few inland provinces with developed infrastructure; and (3) the entry and spatial distribution of equipment and engineering enterprises are highly correlated with local brown industries, whereas high-end service enterprises tend to concentrate in economically developed urban agglomerations. This research hopes to reveal the spatial characteristics of environmental industry in the regional green transition through the decomposition of its value chain and the analysis of the spatial coagglomeration effect.