Date of submission: June 22, 2021; date of acceptance: September 1, 2021. * Contact information: avaniraval@nirmauni.ac.in, Department of Undergraduate Studies in Management, Institute of Management, Nirma University, Ahmedabad, Guja- rat, India, phone: 919375482929; ORCID ID: https://orcid.org/0000-0002-3933-6316. ** Contact information: swatipsaxena@gmail.com, Chimanbhai Patel Institute of Management & research, Gujarat Technological University, Ahmedabad, Gujarat, India, phone: 0919904131066; ORCID ID: https://orcid.org/0000-0003-4585-3448. *** Contact information: shashank@nirmauni.ac.in, Institute of Management, Niram University, Ahmedabad, Gujarat, India, phone: 9099690094; ORCID ID: https://orcid. org/0000-0003-0823-7160. Copernican Journal of Finance & Accounting e-ISSN 2300-3065 p-ISSN 2300-12402021, volume 10, issue 4 Raval, A., Saxena, S., & Thanki, S. (2021). How Carbon Projects Can Add to Sustainable Develop- ment Goals of India’, an Associative Study of CDM Projects. Copernican Journal of Finance & Ac- counting, 10(4), 117–137. http://dx.doi.org/10.12775/CJFA.2021.018 avani raval* Nirma University swati saxena** Gujarat Technological University shashank thanki*** Nirma University how carbon proJects can add to sustainable developMent goals of india’, an associative study of cdM proJects Keywords: carbon credit risk, energy sector, clean development mechanism, sustaina- ble development goals, India. J E L Classification: O31, O13, Q01, Q56. Abstract: Growing concerns of climate change have necessitated a re-examination of business activities and their viability, not only from a financial viewpoint but also so- Avani Raval, Swati Saxena, Shashank Thanki118 cial as well as environmental dimension, popularly known as the ‘Triple Bottom Line approach’. The paper is an attempt to bring around the focus on Clean Development pro- jects that deal with carbon credit in India. The sector is a niche in its numbers but huge in potential. This study mainly examines the CDM project risk associated with carbon credit in the organizations from energy sector that had registered and implemented CDM projects in Gujarat. The analysis is based on purposive data collected for large- -scale CDM projects. Statistical analysis was done through non-parametric tests named descriptive analysis, Spearman correlation analysis, and Mann-Whitney U test applied. Analysis of the result reveals that all the enlisted risk has a high degree of association with large scale projects. Correlation results indicated that all kinds of carbon risks have a meaningful positive relationship with each other irrespective of the phase of the CDM project. Type of organizations (public/private sector) also creates differences in CDM project risks. The findings of the research will assist managers in decision-making about carbon emission project risks.  Introduction Global warming refers to the compounding effect that anthropogenic green- house gas emissions on a natural atmospheric warming phenomenon called the greenhouse effect (Hansen, 2008). In recent years, climate change has become the most important environmental problem. The changing ecosystems affect physical and biological systems and a rise in the temperature causes the extinc- tion of species and would harm society and human health (Kolk & Pinks, 2009). The scientific mainstream guardedly predicted gradual change, with deep ef- fects in the mid-term; increasingly, scientists encounter the signs of climate change manifest in real and present hurricanes, melting polar ice caps, and drought in the Amazon. It is estimated that under current emissions trends, by 2100, the average temperature will increase between 4° and 7°C, with poten- tially catastrophic social and environmental consequences, including rising sea levels, inundation of coastal cities, and large-scale ecosystem transformations (Moutinho & Schwartzman, 2005). The threat of human-induced change to the Earth’s climate due to increased emissions of greenhouse gases (GHGs) is one of the greatest challenges confronting the international community. Both anthro- pogenic emissions (emissions related to human inf luence) of GHGs and their concentration in the atmosphere are increasing (Breidenich, Magraw, Rowley & Rubin, 1998). Though, global platforms putting their efforts to delay the glob- al warming effects results in transitioning to a lower-carbon economy and it requires participation from all economies which are highly contributing to car- bon emission. how cArbon Projects cAn Add to sustAinAble… 119 Developing economies are potential markets to invest in energy supply technologies and so, will be a most critical factor in low-carbon future market. That is why it is very important to encourage low-carbon investment in these economies for an effective global climate policy (Hultman, Pulver, Guimaraes, Deshmukh & Kane, 2012; Pettersson, 2018). In September 2015, the General Assembly adopted the 2030 agenda for sustainable development that includes 17 Sustainable Development Goals (SDGs). The objective was to produce a set of universal goals that meet the urgent environmental, political, and economic challenges. Ziółkowska (2018) indicated that sustainable development is about the use of solutions based on institutional arrangements as well as ethic-and- moral governance leading to a balance among the economic, social, and ecolog- ical spheres. This study focuses on the seventh sustainable development goal (SDG 7), i.e. affordable & clean energy. Among various states in India, Gujarat is the front runner in achieving this goal by 2030 with enhanced international co- operation to facilitate access to clean energy research and technology, includ- ing renewable energy, energy efficiency, and advanced and cleaner fossil-fuel technology, and by promoting investment in energy infrastructure and clean energy technology. United Nations Framework Convention on Climate Change (UNFCCC) stress- es finding out ways and means to control tropical deforestation and forest fires, both to prevent dangerous interference in the climate system, and to achieve sustainable development in the tropics (Moutinho & Schwartzman, 2005). In 1990, United Nations Organization (UNO), to decrease the emission of green- house gases into the atmosphere, released the Kyoto Protocol (Chotalia, 2013). In the year 2005, all the world’s nations met in Kyoto in Japan in 1997 to dis- cuss global warming. As an outcome, Kyoto protocol came into force (which was agreed at the Earth Summit at Rio-de-Janeiro in 1992); its implementation got delayed for more than 7 years because there were difficulties in obtain- ing the necessary number of ratification from the countries, who accounted for 55% of carbon dioxide as compared to emissions level of the year 1990. There is valuable impact on global market by greenhouse gas emission market (IISD, 2009). As a result, under the UNFCCC, industrialized nations entered into a le- gally binding agreement to reduce the collective emissions of greenhouse gases (GHGs) by 5.2% as compared to the 1990 level; calculated at an average over the five years of 2008–2012 (Chotalia, 2013). It provides legally binding emissions targets for Annexure I countries, based on a five-year budget period. UN FCCC has defined the Kyoto protocol mechanism which is presented in figure 1. The Avani Raval, Swati Saxena, Shashank Thanki120 framework of Kyoto Protocol defines three mechanisms for greenhouse gases (GHGs) emission such as Joint Implementation (JI), Clean Development Mecha- nism (CDM), and International Emission Trading (IET). The CDM and JI are in- ternational credit mechanisms to limit GHG emissions. (UNFCCC, 2011; Shah & Baser, 2016). The association between Annexure I and Non-Annexure I coun- try parties defines in CDM mechanisms (Sarkar & Dash, 2010). It provides f lex- ibility concerning the parties’ national implementation of their commitments (Breidenich et al., 1998). Moreover, it also allows f lexibility in the international context by providing for the use of emissions trading and other market-based mechanisms, including mechanisms for cooperative projects between devel- oped and developing countries. The carbon trade allows countries that have higher carbon emissions to purchase the right to release more carbon dioxide into the atmosphere from countries that have lower carbon emissions. Emis- sions trading or Cap and trade include the International emission trading be- tween developed countries (Sivasangari & Rajan, 2016). Figure 1. Kyoto Protocol Mechanisms Figure 1. Kyoto Protocol Mechanisms Figure 2. Number of Approved CDM Projects in India UNFCCC Kyoto Protocol Allowance Based International Emission Trading (Between developed countries) Assigned amount units (AAU) Project Based Clean Development Mechanism (developing and developed countries) Carbon Reduction units (CER) Joint Implementation (Between developed countries) Emission Reduction units (ERU) 1 5 1 69 2 1 2 Total No. of Approved CDM Projects S o u r c e : UNFCCC, 2011. India, one of the fastest-growing economies which has witnessed accelerated economic growth since the early 1990s, initiated economic reforms aiming at market orientation and globalization. It supported the improvement in the en- how cArbon Projects cAn Add to sustAinAble… 121 vironment for businesses and foreign investment, and growth-focused policies, the average economic growth rate between 2005 and 2010 increased to over 8%, but was also accompanied by higher energy consumption. As per IEA re- port 2020, India has set a target growth rate of 9%, which would place it on a path towards becoming a $5 trillion economy by 2024–25 and to make India, the fastest-growing economy in the world. India’s sustained economic growth is placing an enormous demand on its energy resources, energy systems, and infrastructure development (IEA, 2020). India’s integrated energy policy as- sumes an 8% average growth rate for India between 2007 and 2032 (Shukla & Chaturvedi, 2012; GoI, 2006). Various studies discussed the importance of carbon risk. Hultman et al. (2012) analyzed firms’ perceptions towards car- bon market risk and rewards in Brazil and India. The results show that inter- national regulatory jeopardy, financial benefits, and uncertain revenue stream play a major role in CDM project risk. Carbon emission reduction being one of the greatest challenges to businesses risk of firms in the carbon-intensive sec- tor stalling or even abandoning investments in low emitting carbon projects continues to loom (Linares & Pérez-Arriaga, 2009). However, Aifuwa (2020) re- ported that sustainability disclosure level was poor in developing climes com- pared to other developed climes. The transfer of low-carbon technologies to developing countries has a key role to play in reducing carbon emissions as- sociated with future economic development. To achieve this, it requires both vertical and horizontal technology transfer and must facilitate a broader pro- cess of technological change and capacity building within developing countries (Ockwell, Watson, MacKerron, Pal & Yamin, 2008). Butterworth, Subramaniam and Phang (2015) also analyzed carbon risk management with focusing on en- ergy firms of Australia. There is a large number of studies reporting the im- pact of carbon risk on financial performance. Majority of the studies focused on carbon emissions, carbon risk exposure by firms with mainly focused on car- bon-intensive industries, has become one of the dominant themes for business (Labatt & White, 2007; Hoffmann & Busch, 2008; Butterworth et al., 2015). Ac- cording to Clarkson, Li, Pinnuck and Richardson (2015), before formal imple- mentation of regulations, a firm should minimize the impact of carbon risk by utilizing external source of finance to cover the cost of carbon emissions. The initiatives taken for development of carbon-related regulations and policies, firms are more likely to internalize the cost of carbon emissions making carbon risk a significant business consideration. Past research examined carbon risk at the global platform, but very limited studies addressed the issues related to Avani Raval, Swati Saxena, Shashank Thanki122 carbon risks of the Indian economy with a focus on energy sectors. The present study attempts to fill this gap, specifically in the Indian context, and tries to ac- cess the CDM project risk focusing on the energy sector. The research questions this study attempts to answer are: RQ1: What is the need for renewable energy sources for energy generation in India? RQ2: What is the taxonomy of risk associated with CDM projects? RQ3: Are risks associated with CDM projects interrelated? RQ4: Does carbon risk vary regarding ownership of organization (Public/ Private)/ methodology (Solar/Wind) of CDM projects? The rest of the paper is organized as follows: section 2 discusses the theo- retical review of literature focusing on the energy sector scenario in India and carbon risk. Section 3 outlines the research methodology and process. Section 4 presents an analysis of the data followed by a discussion of results and the fi- nal section concludes the study with its implications and states directions for future work. Theoretical Review of the literature Energy sector scenario in India Energy is a basic human need. Developmental statistics confirm a strong corre- lation between energy consumption and economic development (TERI, 2004). The world became a global village due to increasing daily requirements of ener- gy by all populations across the world, while the earth cannot change its form. The need for energy and its related services, to satisfy mankind’s social and economic development, welfare and health, is increasing day by day (Owusu & Asumadu-Sarkodie, 2016). To meet the energy requirements, the role of re- newable energy has become crucial for the power generation, accessibility and reducing consumption of non-renewable energy sources. This will help India to achieve its low carbon development path. Ahead of the Conference of Paris (COP) 21, India submitted its post-2020 climate actions plan to the UNFCCC. In- dia’s INDC builds on its goal of installing 175 gigawatts (GW) of renewable pow- er capacity by 2022. It also supports the need for renewable energy (MNRE, 2019). Parikh, Panda, Ganesh-Kumar and Singh (2009) analyzed carbon emis- sion in the energy sector in India focusing on household final consumption. how cArbon Projects cAn Add to sustAinAble… 123 Lifestyle differences across household expenditure classified into the urban top ten percent account for emissions of 4099 kg per capita per year, while the rural bottom ten percent account only for 150 kg per capita per year. Abdul- lahi (2015) emphasized renewable energy sources as an important alternative source of energy generation. Shukla (2007) analyzed energy sector in India taking time series data to study the issues of energy consumption and supply CO2 emissions, applying the I-O model (Input-Output model). In India, thermal power is a major source of energy generation with renewable energy contrib- uting about 21.95% to it. This shows that there is an untapped market available that can help to delay the critical crisis of global warming. In India, the Ministry of New and Renewable Energy (MNRE) is the nod- al Ministry of the Government of India for all matters which deal with new and renewable energy. The use of renewable resources of energy is rapidly in- creasing worldwide. The economy has started generating electricity from var- ious renewable sources including hydropower, wind, solar, and bioenergy. The Government has defined renewable electricity targets considering short and medium term. It was estimated that the country will be able to install 175 GW capacity renewable energy (IEA, 2020). As per report published by IEA (2020), GoI plan to increase renewable capacity to 275 GW by 2027 (IEA, 2020). The Prime Minister of India announced a new target of 450 GW of renewable elec- tricity capacity, without specifying the date (IEA, 2020). As of November 30, 2020, the installed renewable energy capacity stood at 90.39 GW, of which so- lar and wind comprised 36.91 GW and 38.43 GW, respectively. Biomass and small hydropower constituted 10.14 GW and 4.74 GW, respectively. Power gen- eration from renewable energy sources in India reached 127.01 billion units (BU) in FY20. It is expected that by 2040, around 49% of the total electricity will be generated by renewable energy as more efficient batteries will be used to store electricity. At the same time, due to the increasing population and en- vironmental deterioration, the country faces the challenge of sustainable de- velopment. The gap between demand and supply of power is expected to rise in the future (Kumar & Majid, 2020). Graph 2 also represents the number of CDM projects registered. Energy Industries also contributes to 85% of the to- tal no. of CDM projects. Avani Raval, Swati Saxena, Shashank Thanki124 Figure 2. Number of Approved CDM Projects in India Figure 1. Kyoto Protocol Mechanisms Figure 2. Number of Approved CDM Projects in India UNFCCC Kyoto Protocol Allowance Based International Emission Trading (Between developed countries) Assigned amount units (AAU) Project Based Clean Development Mechanism (developing and developed countries) Carbon Reduction units (CER) Joint Implementation (Between developed countries) Emission Reduction units (ERU) 1 5 1 69 2 1 2 Total No. of Approved CDM Projects S o u r c e : NCDMA Authority, 2021. Carbon Risk Yu and Tsai (2018) examined entrepreneurs’ carbon reduction behavior on their sustainable development from high-carbon-emission industries in Chi- na. There is positive inf luence of carbon emission by firms and significant- ly inf luence firms sustainable development (Yu & Tsai, 2018). Wang and Choi (2016) examined the impact of carbon emission reduction mechanisms on un- certain make-to-order manufacturing. Market-based characteristics of the cap-and-trade mechanism motivate firms with economic benefits to adopt low-carbon technologies and environmental-friendly facilities to curb green- house gases emission. In contrast, administrative issues and outdated tech- nologies negatively impact carbon emissions. Popp, Newell and Jaffe (2010) emphasized three dimensions such as energy, environment, and technologi- how cArbon Projects cAn Add to sustAinAble… 125 cal change. The long-term nature of many environmental problems, such as climate change, makes us understand the evolution of technology as an im- portant part of projecting future impacts. There are mainly three challenges such as technology changes, cost-effectiveness, and environment-friendly en- ergy generation for drafting energy policy for any economy. Chung, Pyo and Guiral (2019) investigated the relationship between carbon risk and a firm’s financial data taking cost of equity. The study also highlighted challenges for financing project and utilization of funds (Chung et al., 2019). Financial chal- lenges negatively inf luence firms to adopt clean technologies (Ashraf, Comyns, Arain & Bhatti, 2019). Carbon-efficient production can be valuable from both operational and risk management perspectives (Trinks, Mulder & Scholtens, 2020). Cadez, Czerny and Letmathe (2019) suggested that managers in devel- oping countries take economic as well environmental concerns into account when planning business strategy (Cadez et al., 2019). Ashraf, Comyns, Tariq and Chaudhry (2020) suggested that market returns, supporting policies, and financial dropping are important antecedents in a developing country context. Krey and Ri ahi (2009) identified two major factors affecting greenhouse gas emissions such as delay in participation and failure in technology in the 21st century. ICAI (2009) covered the concept of carbon credit applied in India. In- dia is part of Non-Annex country and has no restrictions for carbon emission. Larkin, Leiss, Arvai, Dusseault, Fall, Gracie, Heyes and Krewski (2019) suggest- ed that risk assessment and risk management need to be comparable to ensure the long-term reliability and carbon emission reduction standards should be at the international level. This also relates to issues identified by Pawar, Bro- mhal, Carey, Foxall, Korre, Ringrose, Tucker, Watson and White (2015) in their assessment of what needs to be done to improve overall risk management and to remove barriers associated with large-scale deployment. IPCC (2007) and Kim, An and Kim (2015) classified climate change-related risks into six cate- gories: physical risk, regulatory risk, litigation risk, competition risk, produc- tion risk, and reputation risk. Taking into the base, the study classified CDM risk into five categories: Country risk, Registration risk, Performance risk, and Counterparty risk and Market risk. Classification of risks has been considered from literature and presented in graph 3. Avani Raval, Swati Saxena, Shashank Thanki126 Figure 3. Risk associated with CDM project CDM Project Risk Planning Phase Feasibility Risk License Risk Construction Phase Time-over run Risk Capital cost over- run Risk Operation Phase Technology Risk Market Risk Supply Risk Operation Risk Legal Risk Financial Risk Counter party Risk (2020) suggested that market returns, supporting policies, and financial dropping are important antecedents in a developing country context. Krey and Riahi (2009) identified two major factors affecting greenhouse gas emissions such as delay in participation and failure in technology in the 21st century. ICAI (2009) covered the concept of carbon credit applied in India. India is part of Non-Annex country and has no restrictions for carbon emission. Larkin, Leiss, Arvai, Dusseault, Fall, Gracie, Heyes & Krewski (2019) suggested that risk assessment and risk management need to be comparable to ensure the long-term reliability and carbon emission reduction standards should be at the international level. This also relates to issues identified by Pawar, Bromhal, Carey, Foxall, Korre, Ringrose, Tucker, Watson and White (2015) in their assessment of what needs to be done to improve overall risk management and to remove barriers associated with large-scale deployment. IPCC (2007) and Kim, An and Kim (2015) classified climate change-related risks into six categories: physical risk, regulatory risk, litigation risk, competition risk, production risk, and reputation risk. Taking into the base, the study classified CDM risk into five categories: Country risk, Registration risk, Performance risk, and Counterparty risk and Market risk. Classification of risks has been considered from literature and presented in graph 3. Figure 3. Risk associated with CDM project Source: created by authors. S o u r c e : created by authors. Research Methodology The study has been carried out for risk associated with CDM projects regis- tered at a large scale. A comprehensive literature review was conducted us- ing bibliographic database such as scopus, ebsco, google scholar etc. The key words used to identify appropriate literature were carbon risk, energy sec- tor, sustainable practices, developing economy etc. Selected research articles were used to identify key variables for this study. Carbon risks were taken as dependent variables and CDM project methodology and firm ownership were taken as independent variables. A survey instrument was developed including identified variables to analyze association of carbon risk with CDM projects. Test methods which do not require that normality assumptions be met and as a rule do not test hypothesis about population parameters are called nonpara- metric methods or distribution-free methods (Fitzgerald, Dimitrov & Rumrill, 2001). As the sample size is very small and comparing two independent sam- ples, non-parametric tests are warranted for analysis. The primary sampling unit was energy industry firms. We used a clustered sampling method, where how cArbon Projects cAn Add to sustAinAble… 127 clusters represent a group of Indian firms. The energy sector (renewable/non- renewable) has registered the highest number of projects that have taken the base for the selected sector for study. Large scale CDM project (> 15MW) regis- tered by an energy sector organization was taken as the base for the selection of samples. A firm was randomly selected from the group. Employees of the chosen firms were asked to respond. We used personal interviews, telephonic and internet-based methods to administer the survey. Data collected samples from 22 energy firms out of 33 energy firms. By using non-parametric tests, the research attempts to provide insights into the question raised about the risk associated with CDM projects and checks whether the risk involved in the project is independent of the methodology of the project (wind/solar) and firm ownership (public/private). The study also attempts to address the issue of the inter-relationship of carbon risks. The study employs descriptive statistics and Spearman correlation, Mann-Whitney U test to answer the research questions raised in the introduction section. The developed hypothesis was tested using SPSS version 20. Figure 4 describes the step-by-step methodology incorporat- ed indicating sources of data, variables, and analysis techniques. Figure 4. Flow Chart of Methodology Figure 4. Flow Chart of Methodology Source: created by authors. Results and Discussion To check the reliability of instruments used for data collection, Cronbach’s alpha test was applied, and the results obtained are presented in table 1. The summary of independent variables considered for the study is presented in table 2. Descriptive statistics were applied to check the weightage of all categories of risks as shown in table 3. Spearman correlation analysis was used to check whether the risks associated with CDM projects have a significant association with each other or not. Mann-Whitney U test was applied to check differences for carbon risk with type of organization (Public/Private) and methodology of the project (Solar/Wind). Figure 5. Graphical presentation of risk involve in CDM projects S o u r c e : created by authors. Avani Raval, Swati Saxena, Shashank Thanki128 Results and Discussion To check the reliability of instruments used for data collection, Cronbach’s al- pha test was applied, and the results obtained are presented in table 1. The summary of independent variables considered for the study is presented in ta- ble 2. Descriptive statistics were applied to check the weightage of all catego- ries of risks as shown in table 3. Spearman correlation analysis was used to check whether the risks associated with CDM projects have a significant asso- ciation with each other or not. Mann-Whitney U test was applied to check dif- ferences for carbon risk with type of organization (Public/Private) and meth- odology of the project (Solar/Wind). Figure 5. Graphical presentation of risk involve in CDM projects Figure 5. Graphical presentation of risk involve in CDM projects 0 2 4 6 8 10 12 14 Risk Associated with CDM Project Low(Less than 20%) Moderate (20 to 40%) Medium (40 to 70%) High (More than 70%) S o u r c e : created by authors. how cArbon Projects cAn Add to sustAinAble… 129 Table 1. Reliability Test Cronbach’s Alpha Cronbach’s alpha based on standardized items .869 .857 S o u r c e : author’s calculations. The most widely used reliability test that is applied is Cronbach’s alpha (Cron- bach, 1951). The value of test (table 1) is greater than 0.750 which indicates the reliability of the instrument. Table 2. Independent variables summary Methodology of CDM Project Classification of Organization Solar Wind Public Private 10 12 4 18 S o u r c e : author’s calculations. Table 2 represents an independent variables profile taken for analysis. 10 firms have applied solar technology and 12 firms where wind technology has been adopted. Out of 22 firms, the majority of the energy organization samples are from the private sector (18 out of 22). Descriptive Statistics Table 3. Risk level associated with the CDM project N Minimum Maximum Mean Standard Deviation Feasibility Risk 22 1.00 4.00 2.2727 .93513 License Risk 22 1.00 4.00 2.3636 1.09307 Time Over run Risk 22 1.00 4.00 3.0455 .99892 Capital Cost Overrun Risk 22 1.00 4.00 2.7727 .75162 Technology Risk 22 1.00 4.00 2.3182 1.08612 Market Risk 22 1.00 4.00 2.6818 1.24924 Avani Raval, Swati Saxena, Shashank Thanki130 N Minimum Maximum Mean Standard Deviation Supply Risk 22 1.00 4.00 2.3182 1.21052 Operation Risk 22 1.00 4.00 2.4091 1.25960 Legal Risk 22 1.00 4.00 2.5455 1.18431 Financial Risk 22 1.00 4.00 2.9545 1.17422 Counterparty Risk 22 1.00 4.00 2.6818 1.12911 Valid N (list wise) 22 S o u r c e : author’s calculations. Table 3 lists the descriptive statistics of the CDM project risks. The results indi- cate minimum, maximum, mean, and standard deviation of the different catego- ries of risk. Time overrun risk has the highest mean score that is 3.0455 followed by financial risk, counterparty risk, and market risk. The results show a greater standard deviation between operational risk and market risk. During the regis- tration of a project, the planning of execution of the project may vary concern- ing technology adoption in company operation creating risk. The price of CER is expected at the time of contract and at the time of delivery varies and results in market risk. The results indicate that India being a part of non-annexure I coun- tries, companies were highly relying on counterparties. Technology risk leads to operational risk and delay in implementation with the cost of technology lead- ing to support financial risk. The developed project must be able to meet the tar- get emission to gain credits that lead to performance risk (ICAI, 2009). Table 3. Risk level associated… how cArbon Projects cAn Add to sustAinAble… 131 Association of Carbon risk Table 4. Correlation: Risk associated with CDM project Carbon Risk Fe as ib ili ty R is k Li ce ns e R is k Ti m e ov er - -r un R is k Ca pi ta l c os t ov er -r un R is k Te ch no lo gy R is k M ar ke t R is k Su pp ly R is k O pe ra ti on R is k Le ga l R is k Fi na nc ia l R is k Co un te r- -p ar ty R is k Feasibility Risk 0.032 0.025 0.000 0.001 0.302 0.000 0.000 0.484 0.054 0.009 License Risk 0.019 0.645 0.729 0.014 0.055 0.511 0.241 0.325 0.036 Time over-run Risk . 0.01 0.889 0.183 0.584 0.654 0.437 0.07 0.015 Capital cost over-run Risk . 0.049 0.601 0.066 0.021 0.590 0.011 0.186 Technology Risk . 0.351 0.000 0.000 0.061 0.009 0.051 Market Risk . 0.175 0.287 0.001 0.063 0.015 Supply Risk . 0.000 0.016 0.006 0.001 Operation Risk . 0.137 0.001 0.013 Legal Risk . 0.008 0.040 Financial Risk 0.002 Counter-party Risk S o u r c e : author’s calculations. Table 4 presents the correlation analysis expressing the strength of inter-cor- relation among carbon risk parameters. Feasibility risk has an association with all risks except market risk, legal risk, and financial risk which are part of the construction and operation phase of the project. License risk has an association with time over-run risk, market risk, and counter-party risk. Time over-run risk has an association with capital cost over-run risk and counter-party risk. Capital cost over-run risk has an association with technology risk, operation risk, and financial risk. Technology risk has an association with supply risk, op- eration risk, and financial risk. Market risk has an association with legal risk and counter-party risk. Supply risk has an association with operation risk, le- gal risk, financial risk, and counter-party risk. Operation risk has an associa- tion with financial risk and counter-party risk. The result indicates all the risks interrelated with each other. Avani Raval, Swati Saxena, Shashank Thanki132 Carbon risk differs with ownership of firm and methodology of project Carbon risk The firm’s ownership and methodology adopted for the project creates no dif- ference in carbon risk. To check this, Mann-Whitney U Test was applied. Table 5. Mann-Whitney U Test: Carbon risk and Methodology of CDM projects Test Statistics FR LR TOR COR TR MR SR op er at io n Legal Fi na nc ia l Co un te r- pa rt y Mann-Whit- ney U 39.000 58.000 54.500 46.500 55.000 53.000 54.500 51.000 57.000 60.000 59.500 Wilcoxon W 117.000 113.000 109.500 124.500 133.000 108.000 132.500 129.000 112.000 115.000 114.500 Z -1.452 -.136 -.383 -.985 -.345 -.481 -.377 -.619 -.205 .000 -.034 Asymp. Sig. (2-tailed) .146 .892 .701 .325 .730 .630 .706 .536 .838 1.000 .973 Exact Sig. [2*(1-tailed Sig.)] .180b .923b .722b .381b .771b .674b .722b .582b .872b 1.000b .974b a. Grouping Variable: methodology b. Not corrected for ties. S o u r c e : author’s calculations. Table 5 presents a significant difference between carbon risk and the method- ology of the project. The results indicate that there is no difference in carbon risk for the methodology of the projects. This analysis reveals that solar and wind technology projects carry the same level of carbon risk. how cArbon Projects cAn Add to sustAinAble… 133 Table 6. Mann-Whitney U test: Carbon risk and ownership of organization Test Statisticsa FR LR TOR COR TR MR SR op er at io n Legal Fi na nc ia l Co un te r- pa rt y Mann-Whit- ney U 15.000 27.000 26.000 35.000 17.000 34.000 8.000 13.500 35.000 33.500 21.000 Wilcoxon W 25.000 37.000 197.000 45.000 27.000 44.000 18.000 23.500 45.000 43.500 31.000 Z -1.875 -.792 -.900 -.094 -1.693 -.178 -2.479 -1.998 -.088 -.226 -1.332 Asymp. Sig. (2-tailed) .061 .428 .368 .925 .091 .859 .013 .046 .930 .821 .183 Exact Sig. [2*(1-tailed Sig.)] .081b .484b .434b .967b .118b .902b .014b .053b .967b .837b .227b a. Grouping Variable: type of organisation b. Not corrected for ties. S o u r c e : author’s calculations. Table 6 represents the association between carbon risk and ownership of an or- ganization (Public/Private). The results show that there is a significant differ- ence in carbon risk concerning the type of organization in supply risk and op- erational risk. The significant value of supply risk and operational risk is 0.013 and 0.046 respectively. This indicates that in the planning and construction phase there is no difference whether the firm is from a private sector or public sector, but in the operation phase there is a difference in supply risk and opera- tional risk.  Implications and Conclusions The study provides framework to analyze the factors that inf luenced firms for decision-making. This study examines the risk associated with large-scale CDM projects registered by energy sector organizations. Classification of risks pre- sented in a theoretical framework. The empirical results confirmed that all the categories of risk are highly associated with the project. Time over-run risk, capital cost over-run risk and financial risk had a high degree of risk compared to other risks in energy organizations. The reason might be that the company is Avani Raval, Swati Saxena, Shashank Thanki134 not able to achieve targeted emission in a defined time that leads to increased financial cost of the project. Therefore, companies should take care of one of the major parameters that a CDM project should complete on time; otherwise, it leads to capital and financial risk at the time of planning as well as execution of the project. Carbon risk does not have any difference in the methodology adopted for the project. Ownership of organization inf luences creating differ- ences among carbon risk. Apart from carbon risk, two lessons emerged among those firms while en- gaging with CDM projects. First, financial benefits are considered to be prima- ry motivation for undertaking CDM project by most of the respondents. Sec- ondly, one of the primary risk factors considered against these firms’ decisions was international regulatory bodies and its approval process and policies. Risk management includes regulatory, economic, advisory, and community-based, and technology-based approaches. There should be coordinated action at mul- tiple levels and multiple scales are considered best practice in a decision-mak- ing context to protect or improve human health and the natural environment upon which we depend. The results of the study align with and contribute to a growing literature that documents risk and mitigation effects. A practical implication was determined in the present study, namely the organization as- pects. The organization will be able to understand and define a strategy to mit- igate the carbon risk that resulted in effective implementation of the project and achievement of target emission. The results of the study may provide poli- cymakers with insights on carbon risk. It helps government to develop effec- tive energy policies and also help organizations in minimizing project risk. It will facilitate the economy to achieve the sustainable development goal of the economy. Thus, this study contributes to the extension in research field carbon emission and sustainable development practices. The study is limited to large- scale CDM projects registered under energy industries. The study is not focus- ing on mediating the effect on carbon risk. The outcome may serve as a ref- erence for developing countries and other industries of India for CDM project implementations. The study can be extended for empirical study at the global level. 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