An Analysis of Factors Influencing Green Supply Chain Drivers in the Indian Real Estate Sector Using the ISM-DEMATEL Approach
et | 29 juil. 2024
Publié en ligne: 29 juil. 2024
Pages: 83 - 102
DOI: https://doi.org/10.2478/fman-2024-0006
Mots clés
© 2024 Pawan Koul et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The average rate of growth of the Indian construction industry is 9.5%, with the rest to the global average of 5%, as reported by the Indian Green Building Council (igbc.in, n.d). However, India is predicted to have the world’s highest population by 2030, with an estimated population of 1.42 billion (India Population (2022) – World meter, n.d.). India ranks third globally in carbon dioxide emissions. The construction industry is responsible for substantial carbon emissions, accounting for 10.36 metric tons (Blokhin, 2022). In India, the green building concept is still lacking, despite all this, as reported by Sajid, et al. (2020). There is a shortage of qualified building performance evaluators and a lack of training initiatives to educate the workforce on alternative energy-saving methods in the construction industry. Green buildings have the potential to reduce overall building consumption and encourage alternative modes of transportation.
If the green building concept is not implemented, buildings are projected to generate 70% of emissions by 2050. Currently, only 5% of buildings are considered green, according to Setyaning, Wiguna and Rachmawati (2020). Smart market report Produced in Partnership With: World Green Building Trends (2018, n.d) concluded that green practices were not fully utilized. Building owners also have a poor understanding of building costs, as conventional building costs are often assumed to be less than those of green buildings, which is a misconception among real estate developers (Alshamrani, 2020).
It is very crucial to find the prominent green supply chain drivers in real estate projects in India because it can help developers and stakeholders in the construction industry understand the factors that motivate them to adopt sustainable practices. These drivers are related to each other in complex ways, and understanding their interdependencies is essential for developing effective green supply chain strategies. Thus, a comprehensive understanding of the various drivers and their relationships is necessary for developing effective green supply chain strategies that can lead to sustainable practices.
After considering the preceding discussion, the current study aims to pinpoint the following research questions:
RQ1: What are the various green supply chain drivers in real estate projects in India? How are these drivers related to each other? RQ2: What strategies can be developed to design and implement green supply chain drivers based on their relative importance in real estate projects in India?
The main objective of this study is to identify and evaluate the factors that are crucial for driving of green supply chain in real estate projects in India. The research has a dual contribution. Firstly, it aims to enhance comprehension of green supply chain drivers in India’s real estate sector. Secondly, it employs the Interpretive Structural Modeling (ISM) technique to establish a hierarchical structure. The Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis subsequently categorizes the drivers into autonomous, dependent, driving, and linkage clusters. Lastly, the Dynamic Multi-Attribute Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique is utilized to understand the cause-and-effect variables.
Green supply chains are instrumental in fostering sustainability within the construction industry, addressing concerns about the environmental impact and resource conservation. Despite their significance, the concept of green supply chains in construction has been noted as poorly defined. This section seeks to clarify and elaborate on both the drivers and activities associated with green supply chains in the construction context, while also exploring their extended impact on the creation of green buildings.
The real estate industry is under increasing pressure to adopt environmentally sustainable practices, driven primarily by stringent regulations globally. Governments and regulatory bodies are setting strict standards to mitigate environmental degradation and reduce carbon footprints. Recognizing the substantial impact of construction on the environment, there is a growing consensus that companies must transition toward greener practices. Green supply chains provide a systematic approach to this transition, incorporating sustainable practices and aligning with global efforts to combat climate change.
The significance of green supply chains in the construction industry is further underscored by their pivotal role in achieving net zero emissions. As the global community intensifies efforts to combat climate change, the construction sector stands as a major contributor to carbon emissions. Green supply chains offer a strategic pathway for construction companies to align with the ambitious goal of achieving net zero emissions. By integrating sustainable practices, such as responsible material sourcing, energy-efficient technologies, and carbon-neutral construction processes, these supply chains become instrumental in reducing the carbon footprint associated with construction projects. Embracing green supply chains not only supports regulatory compliance and enhances corporate responsibility, but also positions the construction industry as a proactive participant in the global transition toward a sustainable, net zero emission future. This alignment with broader environmental goals is crucial for the real estate sector to play its part in mitigating the impacts of climate change and fostering a resilient, low carbon built environment.
The importance of green supply chains extends to the creation of green buildings. Traditional construction processes are known for resource consumption, waste generation, and environmental degradation. Green supply chains offer a comprehensive framework for addressing these challenges, emphasizing responsible material sourcing, optimized logistics, and the integration of energy-efficient technologies. Beyond meeting regulatory requirements, the adoption of green supply chains enhances a company’s reputation and market competitiveness, attracting environmentally conscious consumers and fostering new business opportunities.
Green supply chains in the construction industry significantly contribute to the emergence of green buildings. By ensuring sustainable practices throughout the supply chain, construction companies can design and construct buildings that adhere to green building standards.
The literature from other countries has been taken in this study because of scarcity of literature on this subject in the Indian context. It is important to note that top-level commitment is necessary to successfully implement green supply chain management (GSCM) in construction projects (Al-Ma’aitah, 2018). Furthermore, with globalization on, companies are becoming more environmentally aware and adopting GSCM practices. Thus, developing sustainable supply chains is critical for reducing carbon footprints (Ghosh, et al., 2020).
The various drivers in the green supply chain include the following:
Government policies and legislation: The Land Acquisition, Rehabilitation and resettlement Act of 2013 has simplified the process of land acquisition and fair compensation for landowners (Gupta, et al., 2017). To increase private participation in the real estate and infrastructure sectors, the Indian government has introduced real estate investment trust (REIT). These trusts must distribute their income to shareholders, and the Securities and Exchange Board of India (SEBI) acts as the regulator (Das and Thomas, 2016). Developer orientation and commitment: Implementing GSCM in construction requires top-level commitment (Al-Ma’aitah, 2018) and growing environmental awareness. However, performance evaluators of buildings must be trained in green buildings (Gupta, et al., 2019). Real estate projects are initiated by developers who approve blueprint drawings, bring materials to the site, and oversee installation, commissioning, and project handover by contractors. Therefore, for the implementation of GSCM, the role of developers can be crucial (Balasubramanian and Shukla, 2017). Figure 1 shows the flow of deliverables in the construction of real estate project. Green financing: The government is responsible for ensuring their implementation as well as framing of rules (Tran, 2021). In the USA, developers of Leadership in energy and environmental design certified buildings are given additional bonuses for increased allowable heights (Qian, et al., 2016). Further, green bonds have emerged as a cost-effective solution for investors and issuers (Maltais and Nykvist, 2021). Green culture and values: Green human resource management practices have been found to enhance the environmental performance of organizations (Shafaei, et al., 2020). Adopting a green culture instills green values and behaviors (Karaduman, et al., 2020). Internal personnel are crucial in recruiting human resources (Ahmad, 2015). Redirecting investments into sustainable infrastructures is essential for transferring clean energy from developed to developing countries to mitigate climate change (Ng, et al., 2021). Building modeling management systems: Building information modeling has been utilized to implement green building concepts (Mohammed, 2021) and address the issues of information transparency (Rogage, et al., 2019). In addition, building information modeling is being considered for crane operations in the future (Edirisinghe, 2019) and can potentially reduce energy and water demands (Alhamami, et al., 2020). Several case studies have demonstrated the benefits of Building information modeling and Building energy modeling in the real estate sector (Gerrish, et al., 2017). The creating, recording, and extracting of information within Building information modeling is done with the help of the procedure shown in Figure 2. Green construction technology adoption: Using prefabrication techniques can enhance the performance of construction. Prefabrication helps prevent quality issues and reduces the need for rework, resulting in cost savings ranging from 5% to 25% (Lavikka, et al., 2021). Green product design capability: Implementing green design concepts requires designers to identify sustainable business strategies (Mohammed, 2021). Research has shown that LEED-certified buildings exhibit higher levels of occupant satisfaction regarding indoor air quality, cleanliness, and maintenance (Lee & Kim, 2008). Intelligent buildings are gaining popularity due to their ability to enhance occupant comfort while minimizing environmental impact (Ghaffarianhoseini, et al., 2016). Architects must consider the location of windows as it can significantly impact the carbon footprint of the building (Hammad, et al., 2019). Green Information and communication technology can drive economic growth and improve environmental performance, and there is a growing demand for green ICT innovation (Cecere, et al., 2019). There is a lack of implementation of agile project management practices in the construction sector (Albuquerque, et al., 2020). The quality of design and construction materials is critical to the success of high-rise buildings, with the design process being the most crucial, followed by construction and procurement (Kabirifar and Mojtahedi, 2019). Green Information and communication technology can drive economic growth and improve environmental performance, and there is a growing demand for green ICT innovation (Cecere, et al., 2019). There is a lack of implementation of agile project management practices in the construction sector (Albuquerque, et al., 2020). The quality of design and construction materials is critical to the success of high-rise buildings, with the design process being the most crucial, followed by construction and procurement (Kabirifar and Mojtahedi, 2019). Green purchasing: The selection of suppliers considers economic, social, and environmental aspects. The weightage of economic aspects is more than environmental and social aspects as per research (Hoseini, et al., 2021). Green warehousing and transportation practices benefit the environment. Developed economies have begun implementing green products (Ali, et al., 2020). Reverse logistics: The environment is less impacted with the use of reverse logistics by managing the construction waste (Vargas, et al., 2021). Customer involvement in GSCM: Consumers are now more conscious of environmental issues and consider the environmental impact of their products. As a result, companies are taking steps to make their supply chain more environment friendly (Güner, et al., 5 C.E.). Real estate projects also expect a reduction in energy costs after occupancy (Times of India Blog, 2021). Enhancement of brand image of real estate developer: The reputation of a real estate developer plays a vital role as real estate investment is a one-time affair. In addition, timely delivery, absence of fraudulent activities, and potential gains are other factors that contribute to building a brand’s reputation ( Developers’ funding and investment capacity: Major foreign investors such as the Government of Singapore investment corporation, Blackstone, Brookfield, and domestic real estate fund management companies have played a crucial role in this funding (Gupta, et al., 2017). Foreign investment in real estate generates employment, facilitates urban development, introduces additional competition, and brings new practices to the host country’s real estate sector (Gholipour, 2013). Knowledge and awareness of green building practices: We need to reduce the demand for energy by 50% to reduce greenhouse gas emissions by 2050. Despite initiating the use of green technologies, neither the Indian nor the global building sector is currently decarbonizing (Graham and Rawal, 2019). Reducing consumption of resources and energy: Obstacles hinder the implementation of sustainable procurement of infrastructure for both contractors and the government (Willar, et al., 2020). Using simulation techniques can decongest traffic in the city and enhance the construction industry’s image (Ying, et al., 2021). Engagement of stakeholders: Utilizing stakeholders in decision-making has numerous benefits. It can lead to better decision-making and build confidence among them. Moreover, involving different stakeholders brings expertise and knowledge to develop a particular property (Sousa, 2012). They also play a role in ensuring compliance with cost, time, and quality during project execution. In addition, stakeholders participate in sourcing materials for the project (Umumararungu and Mulyungi, 2018).
Figure 1.
Picture showing that the developer plays a pivotal role in green supply chain management in the real estate sector
Figure 2.
Process flow for creating, recording, and extracting information using building integration modeling
Figure 3.
Building integration modeling and building energy modeling for designing real estate projects
The significance of the current study lies in the fact that GSCM is still in its early stages of implementation in the Indian real estate sector. Although the literature review reveals that the factors influencing green supply chain drivers have been extensively studied in the manufacturing sector, only a few studies examine the impact of green supply chain drivers in the Indian real estate sector. In addition, there is a dearth of research on the hierarchical relationship between green supply chain drivers using the ISM technique and DEMATEL to understand the cause-and-effect variables. Table 1 shows the prominent green supply chain drivers based on the review of the literature.
Green supply chain drivers in the real estate sector in India
(
| Driver no. | Clubbed driver | Principal drivers | Reference |
|---|---|---|---|
| GD1 | Government policies and legislation |
Real estate investment trust Land acquisition, rehabilitation, and resettlement |
Gupta, et al., 2017; Das and Thomas, 2016 |
| GD2 | Developer orientation and commitment toward green initiation |
Contractor role Architect role Subcontractor role |
Balasubramanian and Shukla, 2017; Al-Ma’aitah, 2018; Gupta, et al., 2019 |
| GD3 | Green financing |
Green financing Green bonds |
Qian, et al., 2016; Tran, 2021; Maltais and Nykvist, 2021 |
| GD4 | Customer awareness and involvement |
Green features Better quality Timely delivery |
Subham Mohanty, 2021; Güner, et al., 5 C.E. |
| GD5 | Building modeling management systems |
Integration of building services Cost reduction Energy modeling |
Mohammed, 2021; Rogage, et al., 2019; Edirisinghe, 2019; Gerrish, et al., 2017 |
| GD6 | Green construction technology adoption |
Prefabrication techniques Modularization |
Lavikka, et al., 2021 |
| GD7 | Green product design capability |
Green design Building plan Orientation of windows Airconditioning |
Lee and Kim, 2008; Mohammed, 2021; Ghaffarianhoseini, et al., 2016; Cecere, et al., 2019; Hammad, et al., 2019; Kabirifar and Mojtahedi,2019 |
| GD8 | Green purchasing |
Supplying environment-friendly raw materials Greening of logistics |
Ali, et al., 2020; Hosseini, et al., 2021 |
| GD9 | Reverse logistics practices |
Construction waste management |
Vargas, et al., 2021 |
| GD10 | Green culture and values |
Recruitment of manpower having green culture and values |
Karaduman, et al., 2020; Shafaei, et al., 2020; Ahmad, 2015; Ng, et al., 2021 |
| GD11 | Enhance reputation and brand image |
No fraudulent activities Future gains |
Aadil Saif, 2014 |
| GD12 | Developers’ investment capability |
Foreign Investment Private wealth channels |
Gupta, et al., 2017; Gholipour, 2013 |
| GD13 | Knowledge and awareness level of green practices |
Green building rating Energy conservation Building code |
Graham and Rawal, 2019 |
| GD14 | Reducing consumption of resources and energy |
Trucks using green fuels for transportation |
Willar, et al., 2020; Ying, et al., 2021 |
| GD15 | Engagement of stake-holders |
Developer–consultant Contractor–supplier |
Sousa, 2012; Umumararungu and Mulyungi, 2018 |
The study’s flow is presented in Figure 4 and is divided into three stages. Stage 1 outlines understanding prominent green supply chain drivers with the help of a review of the literature. Stage 2 details the application of ISM and DEMATEL techniques, and Stage 3 highlights the study’s findings and contributions.
Figure 4.
Research methodology
The participants in the study were from the real estate sector in India. Five experts selected from Delhi and the National capital region with work experience exceedingly more than 25 years in real estate and project management companies were consulted and showed their willingness to participate in this research. Their views were taken on the 15 green supply chain drivers, which we found from a review of the literature and conducting semi-structured interviews. Their responses have been used in ISM as well as the DEMATEL analysis.
The semi-structured interview process implemented in this study adhered to rigorous methodological standards, ensuring a systematic and insightful exploration of the perspectives of five seasoned professionals in the Indian real estate sector (Schatz, 2012). Carefully selected based on their extensive expertise, each participant boasted over 25 years of dedicated experience in leadership roles within reputable real estate and project management companies. The profiles of these participants were characterized by diverse and distinguished roles within their organizations, encompassing executive leadership, project management, sustainability management, and strategic planning. This diversity aimed to ensure a comprehensive representation of perspectives within the industry, considering the multifaceted challenges and opportunities associated with green supply chain dynamics.
The semi-structured interviews were conducted within a specific timeframe between March 2023 and May 2023 to accommodate participants’ schedules, with each interview session lasting approximately 1 h and meticulously designed to strike a balance between structured inquiries focused on the identified drivers for green supply chain and the flexibility required for probing into unanticipated insights. The duration of each session was thoughtfully determined to facilitate in-depth discussions, fostering a rich exchange of ideas and experiences.
The semi-structured nature of the interviews provided a framework of predefined questions while allowing the flexibility necessary to explore emergent themes, ensuring a holistic understanding of the drivers for the green supply chain. Before interviews, participants provided informed consent, emphasizing the purpose, scope, and ethical considerations of the study to uphold participant confidentiality.
Participants were given a list of identified green supply chain drivers and were briefed about the ISM and DEMATEL methodologies before the interview. This preparatory step aimed to familiarize participants with the concepts and methodology, ensuring a more informed and focused discussion during the interviews. Throughout the interviews, probing questions were employed to elicit detailed insights into the participants’ perspectives on the identified green supply chain drivers. Participants were encouraged to share real-world examples and practical experiences, enriching the qualitative data collected.
The basic objective of the semi-structured interviews was to capture the interrelationships between the identified drivers based on the ISM and DEMATEL methodologies. This entailed delving into the nuanced connections and dependencies among the green supply chain drivers, providing a comprehensive understanding of their hierarchical and causal relationships within the Indian real estate sector. The iterative and adaptive nature of the semi-structured interview approach allowed for continuous refinement of subsequent interviews based on evolving insights from earlier sessions, contributing to the depth and richness of the data and enhancing the credibility and validity of the findings.
The ISM technique was employed to identify the most effective green drivers, with cleaner production found to have high driving power and adoption of cuttingedge technology identified as having high dependence power (Sharma, et al., 2021). In addition, ISM has been used to promote eco-design in supply chain management, focusing on economic, environmental, and social practices (Thamsatitdej, et al., 2017). The technique has also been utilized to identify barriers to waste recycling in India, with input material, lack of funds, and government subsidies being the most significant barriers (Chauhan, et al., 2018). ISM and DEMATEL techniques have been used to understand various issues in the diamond mining industry globally, with ISM using (0, 1) to analyze relationships and DEMATEL using a scale of (0, 1, 2, 3, 4) to show the relationship between the cause-and-effect variables (Chauhan, et al., 2018). ISM has also been applied in smart manufacturing, specifically to factors such as data storage, computation, and user interaction in the IT sector (Praneashram, et al., 2021). ISM is implemented with the help of the following steps.
Warfield gave the proposal for the ISM technique in 1973. The process comprises the following steps:
Literature review and semi-structured interviews were used to identify the various green supply chain drivers in real estate projects in India. The contextual relationship among the green supply chain drivers is formulated. Construction of a structural self-interaction matrix (SSIM) to make the comparison of elements pairwise. After checking transitivity, the reachability matrix and the initial and the final matrix are developed. The antecedent set and reachability set are determined. The relationship among the various elements is shown with the help of a digraph. The MICMAC analysis is used for grouping various factors. The model inconsistencies are checked by reviewing the model.
The contextual relationship between factors (i and j) is found. The relationship between the variables is shown with the help of four symbols (i and j). V: it shows the relationship from driver i to j and not from j to i; A: it shows the relationship of the driver from j to i and not from i to j; X: it shows the relationship between both directions i to j and j to i; O: no valid relationship between i and j.
The reachability matrix is formed by substituting 1 and 0 in V, A, X, and O (Kumar Neeraj Jha and Devaya, 2010). The substitution rules are as under:
The V entry in SSIM (i, j) becomes 1 for (i, j) |and 0 for (j, i) The A entry in SSIM becomes 0 for (i, j) and 1 for (j, i) The X entry in SSIM becomes 1 for (i, j) and 1 for (j, i) The O entry in SSIM becomes 0 for (i, j) and 0 for (j, i)
The final reachability matrix is found with the application of the transitivity rule. The transitivity rule states that two variables having a relationship with the same variable are necessarily related.
The intersection of reachability and antecedent set gives us level partitions.
The values obtained from the final reachability matrix are used for constructing a digraph.
Matrix impact cross-reference multiplication is used to classify factors into four groups depending on their level of dependence and driving power. These factors are autonomous, dependent, linkage, and independent.
DEMATEL shows the interrelationships between variables to analyze the cause-and-effect relationships. The various steps are as follows:
Step 1. Obtaining average matrix The experts’ responses are ranked on a Likert scale of 0–5, where 1 = no influence, 2 = low influence, 3 = medium influence, 4 = high influence, and 5 = very high influence. The score of each expert was taken, and the resultant matrix was derived from taking the opinions of five experts:
Step 2. Formalization of initial direct matrix The normalized average matrix Z is derived from the normalized direct matrix D:
Step 3. The total relationship matrix T is calculated The total relationship matrix is calculated with the help of direct or indirect relationships; the total relationship matrix is calculated as follows:
Step 4. Calculating the degree of influence Let Di and Ri values be calculated. Di is the sum of all row values of the total relationship matrix, and Rj values are the sum of all the total relationship matrix’s column values. The net effect is calculated by adding and subtracting all (Di + Rj) and (Di - Rj) values. Step: 5 Impact relation map The impact relationship map is made by plotting all (Di + Rj) and (Di - Rj) values. The positive values in the map show that the drivers are causal variables, and the negative values show that they are effect variables.
The relationship between the drivers was found with the help of formulation of SSIM with expert opinion, followed by the initial reachability matrix, final feasibility matrix, and then making the level partitions, finalizing the hierarchy of factors. Table 2 shows the SSIM matrix.
Structural self-interaction matrix
(
| Code | Driver | GD 1 | GD 2 | GD 3 | GD 4 | GD 5 | GD 6 | GD 7 | GD 8 | GD 9 | GD 10 | GD 11 | GD 12 | GD 13 | GD 14 | GD 15 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GD1 | Government policies | 1 | V | V | V | V | V | V | V | V | V | V | V | V | V | V |
| GD2 | Developer orientation | - | 1 | V | V | V | V | V | V | V | O | O | V | O | O | V |
| GD3 | Green financing | - | - | 1 | V | V | O | O | V | O | O | O | V | O | O | O |
| GD4 | Customer awareness | - | - | - | 1 | V | V | V | V | V | O | V | V | X | X | X |
| GD5 | BIM | - | - | - | - | 1 | V | V | A | V | O | O | A | O | O | O |
| GD6 | Green technology | - | - | - | - | - | 1 | X | X | V | O | O | A | A | V | X |
| GD7 | Green design | - | - | - | - | - | - | 1 | X | X | X | X | V | V | V | V |
| GD8 | Green purchasing | - | - | - | - | - | - | 1 | V | O | O | A | X | O | O | |
| GD9 | Reverse logistics | - | - | - | - | - | - | - | - | 1 | O | X | O | O | V | O |
| GD10 | Green culture | - | - | - | - | - | - | - | - | - | 1 | V | O | O | O | V |
| GD11 | Brand image | - | - | - | - | - | - | - | - | - | - | 1 | O | O | O | O |
| GD12 | Fund management | - | - | - | - | - | - | - | - | - | - | 1 | V | V | V | |
| GD13 | Knowhow of green practices | - | - | - | - | - | - | - | - | - | - | - | - | 1 | V | O |
| GD14 | Reducing resource consumption | - | - | - | - | - | - | - | - | - | - | - | - | - | 1 | O |
| GD15 | Stakeholders’ engagement | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 1 |
The pairwise relationship was identified through the semi-structured interviews conducted with the experts in the form of an SSIM matrix presented in Table 2. Next, this matrix was converted into initial reachability matrix by substituting V, A, X, and O with 1s and 0s. After this, the Initial reachability matrix was checked for transitivity, and we obtained the final reachability matrix as shown in Table 3. After developing the final reachability matrix, level partitioning was conducted for each variable, and the reachability set, the antecedent set, as well as the intersection set were identified, as shown in Table 4. The connecting variables are drawn in the form of an ISM model based on their relationship at each level.
Final reachability matrix
(
| Code | Driver | GD 1 | GD 2 | GD 3 | GD 4 | GD 5 | GD 6 | GD 7 | GD 8 | GD 9 | GD 10 | GD 11 | GD 12 | GD 13 | GD 14 | GD 15 | Driving power |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GD1 | Government policies | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 15 |
| GD2 | Developer orientation | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 13 |
| GD3 | Green financing | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 12 |
| GD4 | Customer awareness | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 12 |
| GD5 | BIM | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 11 |
| GD6 | Green construction technology | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 11 |
| GD7 | Green design | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 |
| GD8 | Green purchasing | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 9 |
| GD9 | Reverse logistics | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 5 |
| GD10 | Green culture | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 6 |
| GD11 | Brand image | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| GD12 | Fund management | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 8 |
| GD13 | Awareness of green practices | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 11 |
| GD14 | Resource consumption | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 11 |
| GD15 | Stakeholder engagement | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 11 |
| Dependence power | 1 | 2 | 3 | 10 | 11 | 13 | 14 | 13 | 14 | 8 | 13 | 12 | 13 | 14 | 13 | - |
Level partitions
(
| Iteration 1 | ||||
| Factors | Reachability set | Antecedent set | Intersection | Level |
| GD1 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 | 1 | 1 | - |
| GD2 | 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15 | 1, 2,3 | 2,3 | - |
| GD3 | 2,3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15 | 1, 2, 3 | 2, 3 | - |
| GD4 | 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15 | 1, 2, 3, 4, 8, 9, 12, 13, 14, 15 | 4, 8, 9, 12, 13, 14, 15 | - |
| GD5 | 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15 | 1, 2, 3, 4, 5, 6, 8, 10, 12, 13, 14, 15 | 5, 7, 9, 10, 12, 13, 14, 15 | - |
| GD6 | 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 | 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15 | 5, 6, 7, 8, 10, 12, 13, 14, 15 | - |
| GD7 | 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 | 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 13, 14, 15 | 6, 7, 8, 9, 10, 11, 13, 14, 15 | - |
| GD8 | 4,5,6, 7, 8, 9, 12, 13, 14,15 | 1, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15 | 4, 5, 6, 7, 8, 11,12, 13, 14,15 | - |
| GD9 | 4, 7, 9, 11, 14 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15 | 4, 7, 9, 11, 14 | i |
| GD10 | 6, 7, 9, 10, 11, 15 | 1, 2, 4, 5, 6, 7, 10, 11 | 6,7,10,11 | - |
| GD11 | 7, 8, 9, 10, 11, 12, 13, 14, 15 | 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15 | 7, 8, 9, 10, 11, 13, 14, 15 | - |
| GD12 | 4, 5, 6, 8, 12, 13, 14, 15 | 1, 2, 3, 4, 5, 6, 7, 11, 12, 13, 14, 15 | 4, 5, 6, 12, 13, 14, 15 | - |
| GD13 | 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15 | 1, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15 | 4, 5, 6, 7, 8, 11, 12, 13, 14, 15 | - |
| GD14 | 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15 | 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15 | - |
| GD15 | 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15 | 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15 | 4, 5, 6, 7, 11, 12, 13, 14, 15 | - |
| Iteration 2 | ||||
| GD1 | 1, 2, 3, 5, 6, 8, 10, 12, 13, 15 | 1 | 1 | - |
| GD2 | 2, 3, 5, 6, 8, 10, 12, 13, 15 | 1, 2,3 | 2,3 | - |
| GD3 | 2,3, 5, 6, 8, 10, 12, 13,15 | 1, 2, 3 | 2, 3 | - |
| GD4 | 5, 6, 8,10, 12, 13, 15 | 1, 2, 3, 8, 12, 13,15 | 8, 12, 13,15 | - |
| GD5 | 5, 6, 8, 10,12, 13, 15 | 1, 2, 3, 5, 6, 8,10, 12, 13, 15 | 5, 6,8, 10, 12, 13, 15 | - |
| GD6 | 5, 6, 8, 10, 12, 13,15 | 1, 2, 3,5, 6, 8, 10, 12, 13, 15 | 5, 6, 8, 10, 12, 13, 15 | - |
| GD7 | 6, 8, 10, 12, 13, 15 | 1, 2, 3, 5, 6, 8, 13, 15 | 6, 8, 10, 13, 15 | - |
| GD8 | 5,6, 8, 12, 13,15 | 1, 2, 3, 5, 6, 8, 12, 13,15 | 5, 6, 8, 12,13,15 | ii |
| GD10 | 6, 10, 15 | 1, 2, 5, 6,10 | 5, 6, 12, 13, 15 | - |
| GD11 | 8, 10, 12, 13, 15 | 1, 3, 5, 6,,8, 10, 13,15 | 8, 10,13, 15 | - |
| GD12 | 5, 6, 8, 12, 13, 15 | 1, 2, 3, 5, 6, 12, 13,15 | 5, 6, 12, 13, 15 | - |
| GD13 | 5, 6, 8,12, 13,15 | 1, 2, 3,5, 6, 8,12, 13,15 | 5, 6, 8, 12, 13, 15 | - |
| GD14 | 5,6,8,12,13, 15 | 1, 2, 3, 5, 6, 8, 12, 13, 15 | 5, 6, 8, 12, 13, 15 | - |
| GD15 | 5, 6, 8,12, 13, 15 | 1, 2, 3, 5, 6, 10, 12, 13, 15 | 5, 6, 12, 13, 15 | - |
| Iteration 3 | ||||
| GD1 | 1, 2, 3, 10 | 1 | 1 | - |
| GD2 | 2, 3, 10 | 1, 2,3 | 2,3 | - |
| GD3 | 2, 3,10 | 1, 2, 3 | 2, 3 | - |
| GD4 | 10,11 | 1, 2, 3 | - | - |
| GD5 | 10 | 1, 2, 3, 10 | 10 | iii |
| GD6 | 10 | 1, 2, 3, 10 | 10 | - |
| GD7 | 10 | 1, 2, 3, 10 | 10 | - |
| GD10 | 10 | 1,2,10 | 10 | |
| GD11 | 10 | 2, 3, 10 | 10 | - |
| Iteration 4 | ||||
| GD1 | 1, 2, 3 | 1 | 1 | - |
| GD2 | 2, 3 | 1, 2,3 | 2,3 | - |
| GD3 | 2, 3 | 1, 2, 3 | 2, 3 | iv |
| Iteration 5 | ||||
| GD1 | 1 | 1 | 1 | v |
Figure 5 shows the ISM model, which comprises five hierarchical levels. Independent drivers are present at the lowest level, linkage drivers at the middle level, and dependent drivers at the highest level. The key factors include government policies, developer orientation, green financing, and green culture. The government’s role is crucial in boosting green real estate development projects by introducing policies. Developers’ interest in launching green projects can be made possible with green financing schemes offered by banks and financial institutions. The imbibing of green culture in the organizations is also important.
Figure 5.
Hierarchy structure of green supply chain drivers using ISM technique
The four clusters in the MICMAC analysis categorize the factors as autonomous, dependent, linkage, and independent after examining the driving and dependence power of variables. Figure 6 shows that reverse logistics and green culture fall under the dependent cluster with high dependence but low driving power. Factors such as building integration modeling, customer awareness, green construction technology, green product design, green purchasing, brand image, fund management, knowledge and awareness about green practices, reduction in consumption, and engagement of stakeholders fall into the linkage cluster with high dependence and high driving power. The most important driving factors for the implementation of drivers for green supply chains in real estate projects in India are government policies, the orientation of developers, and green financing, which have high driving power but low dependence power and fall into independent clusters. These factors play a significant role in enabling green supply chain drivers in the real estate sector in India. No driver falls in the autonomous cluster with both less driving power as well as dependence power.
Figure 6.
MICMAC analysis
The cause-and-effect groups of factors can be classified using the D–R score method as per Table 5. From the table 5 we can see that the score having the highest D–R value is attributed to government policies and legislation (GD1), followed by developer orientation and commitment toward green (GD2), green financing (GD3), and customer awareness (GD4). The figure 7 shows the variables having highest D-R values in the positive side having values greater than zero are called causal variables. The government’s policies are most important for the implementation of drivers of green supply chains in the real estate industry in India. On the other hand, the remaining 11 factors are classified in the dependent group due to their relatively lower scores.
Figure 7.
Causal diagram using DEMATEL approach
Degree of influence
(
| Factor | Di | Ri | Di + Ri | Di - Ri |
|---|---|---|---|---|
| GD1 | 2.332108 | 0.609406 | 2.941514 | 1.722702 |
| GD2 | 2.165233 | 0.728962 | 2.894195 | 1.436271 |
| GD3 | 2.046763 | 0.796619 | 2.843382 | 1.250144 |
| GD4 | 1.335043 | 0.863433 | 2.198477 | 0.47161 |
| GD5 | 1.388685 | 1.511477 | 2.900162 | -0.12279 |
| GD6 | 1.312976 | 1.638 | 2.950976 | -0.32502 |
| GD7 | 1.096431 | 1.638716 | 2.735147 | -0.54229 |
| GD8 | 1.096431 | 1.388115 | 2.484545 | -0.29168 |
| GD9 | 1.221623 | 1.815813 | 3.037435 | -0.59419 |
| GD10 | 1.179119 | 1.947681 | 3.1268 | -0.76856 |
| GD11 | 1.085274 | 1.567534 | 2.652808 | -0.48226 |
| GD12 | 1.108241 | 1.5315 | 2.639741 | -0.42326 |
| GD13 | 1.241029 | 1.664408 | 2.905437 | -0.42338 |
| GD14 | 1.140528 | 1.625072 | 2.765599 | -0.48454 |
| GD15 | 1.181771 | 1.604518 | 2.786289 | -0.42275 |
Driving sustainability through strategic policy interventions: Government policy should promote the introduction of financial incentives, tax credits, or expedited approvals specifically for real estate projects that attain recognized green certifications, such as Indian green building council or Leadership in energy and environmental design (Smith, 2015). This proactive approach encourages developers to prioritize sustainability within their supply chains. In addition, government policy should mandate a comprehensive environmental impact assessment as an integral component of the approval process for real estate projects. This mandated assessment should encompass thorough evaluations of the entire supply chain, thereby fostering transparency and accountability in environmental practices. Furthermore, government policy should play a pivotal role in establishing and rigorously enforcing energy efficiency standards for buildings. Such standards act as a catalyst, encouraging developers to seamlessly integrate energy-efficient technologies into both the construction methods and operational facets of the built environment. Government policy should also take the lead in enforcing stringent regulations related to waste management during and after construction phases. This includes the mandate for recycling construction waste and providing incentives for projects that exhibit minimal waste generation through adoption of efficient construction practices. Moreover, government policy should allocate substantial funds toward research and development in sustainable construction technologies and practices. This strategic allocation aims to stimulate innovation within the industry, thereby making green supply chain solutions more accessible and cost-effective for developers. Finally, government policy should actively facilitate the establishment of platforms for continuous dialog between governmental bodies and the real estate industry. This collaborative approach ensures that policies remain adaptable to industry dynamics, fostering a cooperative environment crucial for the successful implementation of green supply chain drivers.
To enhance developer orientation and commitment toward GSCM in the real estate industry, several strategic measures can be implemented. Acknowledging that GSCM implementation requires top-level commitment, it is imperative to foster a culture of environmental consciousness among developers. Government bodies, in collaboration with industry stakeholders, should initiate awareness campaigns and training programs to educate developers on the principles and benefits of GSCM, emphasizing the top-level commitment necessary for its successful integration (Al-Ma’aitah, 2018).
To bridge the knowledge gap identified by R. Gupta, et al. (2019), where performance evaluators of buildings need training in green buildings, policymakers should design and mandate training programs specifically tailored for real estate developers. These programs should cover essential aspects of green building practices, sustainable construction technologies, and the implications of GSCM. This targeted education will empower developers to make informed decisions that align with sustainability goals.
Given that real estate projects are initiated and overseen by developers, who play a pivotal role in approving blueprint drawings, sourcing materials, and supervising the entire construction process, their commitment becomes central to the successful implementation of GSCM (Balasubramanian and Shukla, 2017). Government policy should encourage and incentivize developers to incorporate GSCM principles into project planning and execution. Financial incentives, tax credits, and streamlined approvals could be offered to projects that adhere to recognized green certifications, fostering a tangible commitment to sustainable practices.
Moreover, regulatory bodies should introduce requirements for developers to undergo training on GSCM principles as a prerequisite for project approval. This ensures that developers possess the necessary knowledge and understanding to effectively implement GSCM throughout the project lifecycle. Collaboration with industry experts and academia can aid in the development of standardized training programs tailored to the real estate sector.
Continuous industry engagement, facilitated through platforms for dialog and collaboration between government bodies and developers, is essential. These forums should serve as spaces for sharing best practices, discussing challenges, and collectively shaping policies that promote GSCM. By fostering an environment of collaboration and continuous learning, developers can be encouraged to embrace a long-term commitment to sustainable construction practices.
To advance sustainable building initiatives, a multifaceted approach is necessary, focusing on green financing mechanisms. Drawing on insights from Tran (2021), government intervention is imperative to establish comprehensive rules and regulations specifically tailored for green building projects. Policymakers should actively participate in the formulation and implementation of these regulations to ensure their effectiveness and adherence across the real estate sector. Taking inspiration from the USA, where developers of LEED-certified buildings receive additional bonuses for increased allowable heights (Qian, et al., 2016), governments globally should consider implementing similar incentives. Such measures not only encourage developers to pursue green certifications, but also contribute to the overall advancement of sustainable construction practices.
Moreover, the adoption of green bonds, as highlighted by Maltais and Nykvist (2021), can be explored as a viable financial tool for both investors and issuers. Government policies should facilitate the creation and utilization of green bonds, providing cost-effective solutions that attract investments for sustainable building projects. By incentivizing and supporting the issuance of green bonds, governments can effectively channel capital toward environmentally conscious developments.
To effectively engage customers in the realm of real estate projects and sustainability, strategic measures should be implemented in response to the growing environmental consciousness among consumers. Companies should proactively communicate their commitment to environmental responsibility, aligning with the shift in consumer preferences outlined by Güner, et al. (5 C.E.). A robust customer engagement strategy involves transparently showcasing efforts to make the entire supply chain more environmentally friendly, emphasizing sustainable sourcing, construction practices, and energy-efficient technologies.
Moreover, real estate developers should address customer expectations regarding energy costs postoccupancy. This involves implementing measures to reduce energy consumption within the constructed spaces, such as integrating energy-efficient appliances, utilizing renewable energy sources, and designing buildings with sustainability in mind. Developers should effectively communicate these features to potential customers, emphasizing the long-term benefits of reduced energy costs associated with environmentally conscious real estate projects.
In addition, creating awareness among consumers about the broader environmental impact of real estate projects and how their choices contribute to sustainable practices is essential. Engaging customers through informative campaigns, community outreach programs, and interactive platforms can foster a sense of shared responsibility and encourage them to make environmentally conscious decisions in their real estate choices.
Ultimately, a customer engagement strategy should focus on transparency, education, and aligning real estate offerings with the values and expectations of environmentally conscious consumers. By fostering a strong connection between sustainability efforts and customer choices, real estate developers can not only meet market demands, but also contribute to the broader goal of creating environmentally responsible and energy-efficient living spaces.
We have identified 15 factors influencing the implementation of drivers of green supply chain in the real estate industry in India, and the interrelationship between the drivers is understood with the help of ISM. Further, DEMATEL has been used to understand the cause-and-effect drivers. The ISM results in a model with five-tier hierarchies. MICMAC classifies the factors into four clusters. The DEMATEL method classifies four factors as causal factors and 11 factors as those which are affected by causal factors. As per the ISM model, the independent factors are government policies and legislation, developer’s orientation and commitment toward green, green financing, and green culture and values. As per the DEMATEL method, the causal factors are government policies and legislation, developer orientation and commitment toward green, green financing, and customer awareness.
In the construction sector, the integration of drivers of green supply chain becomes paramount, involving the alignment of construction processes with eco-friendly initiatives, emphasizing the use of sustainable materials, and promoting energy-efficient technologies. Notably, the imperative to green the supply chain is particularly pronounced in the construction industry, given its significant environmental footprint. The identified factors influencing drivers of green supply chain in the real estate industry resonate profoundly in the construction domain, emphasizing the need for sustainable practices, resource efficiency, and environmental responsibility. Addressing challenges such as government policies and legislation, developer commitment to green initiatives, and the availability of green financing in both the real estate and construction sectors is crucial for fostering sustainable and environmentally responsible practices.
Our study has successfully identified and analyzed 15 influential factors shaping drivers of green supply chain within the Indian real estate and construction sectors. The application of the ISM methodology has facilitated a nuanced understanding of the interrelationships among these drivers, culminating in the development of a five-tier hierarchical model. Moreover, the utilization of the DEMATEL approach has allowed us to discern the cause-and-effect relationships among these drivers. The MICMAC analysis contributed to the categorization of these factors into four clusters based on driving and dependence power, providing additional insights into the relative influence and interdependence among them.
These findings offer a robust foundation for understanding the intricate dynamics of drivers for green supply chain in the Indian real estate and construction sectors. The insights hold significant implications for industry practitioners, policymakers, and researchers, providing valuable guidance for sustainable and environmentally responsible practices within these vital sectors. By embracing and adapting these drivers for green supply chain, the real estate and construction industries can collectively contribute to a more sustainable and environmentally conscious future.
One limitation of using ISM and DEMATEL in modeling green supply chain implementation drivers in the real estate sector is their reliance on subjective expert opinions. ISM and DEMATEL require the input of experts knowledgeable in the field of study to identify and rank the drivers to implementation. However, these experts may have different opinions on what the most important drivers are, and their judgments may be influenced by their personal biases and experiences. This subjectivity can lead to inconsistencies in the results and undermine the model’s reliability.
Another limitation of ISM and DEMATEL is their inability to capture the dynamic nature of the real estate sector. Real estate projects are complex and constantly changing, and the barriers to green supply chain implementation may shift over time. However, ISM and DEMATEL are static models that do not account for environmental or industry changes.