Economic Implications of Renewable Energy Adoption in Logistics and Its Impact on Transportation Costs and Food Price Inflation

Economic Implications of Renewable Energy Adoption in Logistics and Its Impact on Transportation Costs and Food Price Inflation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Economic Implications of Renewable Energy Adoption in Logistics and Its Impact on Transportation Costs and Food Price Inflation Janardhana Anjanappa, Shridhar M Samant This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7549450/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The transition to renewable energy in logistics is increasingly recognized as a crucial step toward sustainability. However, its economic implications, particularly on transportation costs and food price inflation, remain underexplored. This study examines the relationship between renewable energy adoption, logistics costs, and food price inflation using descriptive statistics, correlation analysis, time-series modelling (ARIMA, VAR), multiple linear regression, and Granger causality tests. The findings indicate that renewable energy adoption leads to short-term increases in logistics costs due to infrastructure investments, though long-term benefits may materialize. A strong positive correlation was found between renewable energy adoption and food price inflation, suggesting that green logistics integration could drive initial cost surges. The Granger causality test confirmed that renewable adoption influences logistics costs but does not directly cause food price inflation, implying the role of additional macroeconomic factors. Policy interventions, such as targeted subsidies, infrastructure investments, and gradual transition strategies, are recommended to balance sustainability with economic stability. This research contributes to the growing body of literature on sustainable logistics and offers insights for policymakers and industry stakeholders. Renewable Energy Logistics Costs Food Price Inflation Sustainable Transportation Green Supply Chains 1. Introduction The global logistics sector, a critical artery of international commerce and economic development, is undergoing a profound transformation driven by the imperative of sustainability. Rising concerns over climate change, energy security, and environmental degradation have made the integration of renewable energy sources into logistics operations a pivotal strategy for reducing the carbon footprint of supply chains (Khan, 2019; Khan et al., 2019). This transition encompasses the adoption of electric vehicles (EVs), biofuels, solar-powered infrastructure, and smart technologies, and is increasingly recognized not merely as an environmental obligation but as a strategic component of modern supply chain management (Roy & Mohanty, 2024; Barton & Thomson, 2021; Alagarsamy et al., 2021). In emerging economies such as India, where logistics costs constitute nearly 14% of GDP, the shift toward renewable-powered logistics presents both opportunities and risks (Sharma et al., 2021; Havenga, 2010; Basavaraj et al., 2012). However, the pathway to a green logistics paradigm is fraught with complex trade-offs. The initial capital outlay required for renewable energy infrastructure including EV charging stations, biofuel processing plants, and cold-chain facilities often results in short-term escalations in transportation costs (Singh et al., 2024; Sobha et al., 2023; Meneghetti et al., 2018). These increased logistics costs can ripple across the economy, particularly through food supply chains, where transportation is a major cost component (De & Rout, 2008; Fulzele et al., 2019). Empirical evidence suggests that fluctuations in transportation costs significantly influence food inflation, especially in developing countries where supply chains are vulnerable to inefficiencies and shocks (Shively & Thapa, 2017; Mishra & Roy, 2012; Bhattacharya, 2016; Bhattacharya & Sen Gupta, 2018). Moreover, food price volatility in India has often been linked to logistics bottlenecks, oil price shocks, and systemic inefficiencies in supply chain management (Varghese, 2017; Lahiri & Ghosh, 2014; Gupta & Siddiqui, 2014; Anand et al., 2014; Moorthy & Kolhar, 2011; Goswami, 2010). The challenge is further amplified by broader macroeconomic and environmental dynamics. Research indicates that food inflation in India is shaped not only by logistics inefficiencies but also by climate variability, international price transmission, and policy constraints (Mitra & Chattopadhyay, 2017; Chandrasekhar, 2012; Shahani et al., 2024; Mishra & Agarwal, 2021; Dash & Lugauer, 2024). Climate-induced risks, such as monsoon variability, contribute to food price volatility (Birthal et al., 2019), while cold-chain gaps result in significant postharvest losses (Maheshwar & Chanakwa, 2006). Furthermore, renewable energy integration itself is influenced by land use conflicts, trade competitiveness, and financial barriers (Kiesecker et al., 2019; Desai, 2021; Yu et al., 2022; Murshed et al., 2020). Studies also highlight that the introduction of green packaging and circular economy practices in logistics can help offset rising costs, though adoption barriers remain (Lingaitienė & Burinskienė, 2024; Panghal et al., 2024; Kashem et al., 2024). In the Indian context, supply chain disruptions such as those caused by COVID-19 highlighted the fragility of food logistics, reinforcing the link between transport costs and inflationary pressures (Reardon et al., 2020; Sudan & Taggar, 2021). Oil price fluctuations, global macroeconomic conditions, and monetary policy interventions further complicate the nexus between logistics costs and food inflation (Sultan et al., 2020; Dar & Asif, 2023; Kaushik & Shastri, 2024; Nayak & Jena, 2024). At the same time, renewable adoption in logistics is associated with inflationary risks during transition phases, as green technologies and infrastructure investments tend to increase input costs (Mogale et al., 2020; Gao et al., 2019; Gawusu, 2024; Zhang et al., 2024). Yet, long-term benefits such as reduced fossil fuel dependency, improved efficiency, and stabilized energy costs remain compelling (Bekun, 2022; Gorjian et al., 2022; Lu et al., 2020; Amir et al., 2024). Despite this growing body of literature, a significant gap persists in understanding the integrated dynamics between renewable energy adoption in logistics, its impact on transportation costs, and the resultant effect on food price inflation (Gupta & Dhar, 2022; Kar & Datta, 2020). While some studies examine logistics costs (Jagtap et al., 2020; Mohapatra et al., 2021) or food inflation drivers (Nair & Eapen, 2013; Sonna et al., 2014; Malhotra & Maloo, 2017), few provide a comprehensive empirical analysis of their interconnections. Addressing this gap is particularly crucial for developing economies such as India, where food security, energy transitions, and inflation stability intersect (Anand, 2014; Gunatilake et al., 2011; Mukherjee et al., 2019). Therefore, this study seeks to investigate the economic implications of renewable energy adoption in logistics, focusing specifically on its dual impact on transportation costs and food price inflation. By employing a combination of descriptive statistics, time-series modelling, regression analysis, and Granger causality tests, the research aims to disentangle these relationships and provide policy-relevant insights. In doing so, the paper contributes to debates on how economies can balance environmental sustainability with inflation stability in an era of energy transition. 2. Literature Review Renewable Energy Adoption in Logistics The logistics sector in India is steadily transitioning toward renewable energy sources such as solar, wind, and biofuels, with the objective of reducing dependency on fossil fuels and meeting sustainability goals (Roy & Mohanty, 2024; Basavaraj et al., 2012). Adoption strategies include electric and hybrid vehicles, biofuel-powered fleets, and renewable-powered cold-chain systems (Kumar & Bharj, 2020; Kashem et al., 2024; Amir et al., 2024). While these approaches promise long-term cost savings and emissions reductions, they often require significant upfront investments in vehicles, infrastructure, and charging networks (Singh et al., 2024; Sobha et al., 2023; Mohapatra et al., 2021). Global studies show that energy storage, biofuel strategies, and renewable-powered warehousing can play a critical role in accelerating this transition (Barton & Thomson, 2021; Gunatilake et al., 2011; Meneghetti et al., 2018). Moreover, consumer demand for sustainable products and green logistics practices further drives adoption, with studies linking green consumption values to the diffusion of renewable-powered supply chains (Alagarsamy et al., 2021; Panghal et al., 2024). However, barriers such as limited policy support, technological readiness, and infrastructure gaps constrain large-scale adoption, particularly in developing economies (Khanna, 2022; Desai, 2021; Kiesecker et al., 2019). Despite these challenges, the literature identifies opportunities for integrating renewables into logistics networks. Circular economy approaches, sustainable packaging, and IoT-enabled logistics planning can support efficiency gains and cost savings in renewable-powered supply chains (Lingaitienė & Burinskienė, 2024; Singh & Roy, 2020; Helo & Luomala, 2011). Nonetheless, questions remain on whether such interventions can offset the short-term cost pressures associated with energy transitions in logistics. Impact on Transportation Costs The cost implications of renewable energy adoption in logistics are complex and often context-dependent. While renewable-powered fleets reduce dependency on fossil fuels in the long run, their deployment requires capital-intensive infrastructure, which may initially drive transportation costs higher (Sharma et al., 2021; Jagtap et al., 2020). Research from India highlights how logistics sprawl and inefficiencies add to transportation costs, especially in urban food supply chains (Mohapatra et al., 2021; Mogale et al., 2020). Empirical studies demonstrate that logistics cost structures are shaped by fuel prices, technological advances, and infrastructure availability. Oil price shocks in particular have strong inflationary effects, contributing to cost escalation in transport operations (Varghese, 2017; Sultan et al., 2020; Kaushik & Shastri, 2024). Cost measurement studies further suggest that logistics costs can reach up to 14% of GDP in developing economies, significantly constraining competitiveness (Havenga, 2010; De & Rout, 2008). International experiences indicate that cost optimization through renewable adoption can be achieved over time by deploying solar-powered cold chains, optimized distribution systems, and automation technologies (Meneghetti et al., 2018; Dieaconescu et al., 2022; Tsolakis et al., 2023). Dynamic models confirm that renewable energy investments are sensitive to macroeconomic conditions, particularly oil prices, exchange rates, and trade integration patterns (Gao et al., 2019; Murshed et al., 2020; Yu et al., 2022). For India, this suggests that transport costs during the transition phase will remain volatile unless supported by robust subsidies and infrastructure investments (Saravanan et al., 2021; Lu et al., 2020). Link between Transportation Costs and Food Price Inflation The relationship between logistics costs and food inflation is well established but highly nuanced. Rising transportation costs can increase food prices by amplifying the costs embedded in agricultural supply chains (Shively & Thapa, 2017; Bhattacharya & Sen Gupta, 2018). However, in some cases, higher costs force supply chain actors to innovate, optimize routes, and reduce inefficiencies, leading to mixed outcomes (Kharaishvili & Gechbaia, 2023; Rathore et al., 2021). Indian evidence suggests that food price inflation is influenced by multiple structural drivers, including fuel price shocks, weak storage systems, and global market volatility (Mishra & Roy, 2012; Bhattacharya, 2016; Goswami, 2010; Chandrasekhar, 2012; Birthal et al., 2019). Policy failures and market rigidities, such as those stemming from the APMC Act, have further contributed to persistent inflationary pressures in food markets (Neha Tomar, 2013; Nair & Eapen, 2013). Supply chain disruptions during COVID-19 also underscored how vulnerable India’s food logistics networks remain to external shocks (Reardon et al., 2020; Sudan & Taggar, 2021). Beyond domestic dynamics, studies indicate that international food and fuel markets exert significant pressure on Indian food prices. Oil price volatility and global commodity price movements have consistently transmitted inflationary effects into the Indian economy (Sultan et al., 2020; Dash & Lugauer, 2024; Zhang et al., 2024). Structural models confirm that food inflation in India is shaped by monetary policies, climate shocks, and trade integration (Anand et al., 2014; Mishra & Agarwal, 2021; Dar & Asif, 2023). Cold chain inefficiencies, which result in postharvest losses of perishable goods, also exacerbate inflation (Maheshwar & Chanakwa, 2006). In the broader sustainability literature, researchers emphasize that renewable adoption in food logistics may itself act as a double-edged sword. On one hand, renewable-powered cold storage and transport can stabilize food supply chains over the long run (Gorjian et al., 2022; Abid & Saqlain, 2023; Umar & Wilson, 2024). On the other hand, initial cost surges in renewable infrastructure can exacerbate food price inflation during transition phases (Gawusu, 2024; Malhotra & Maloo, 2017). This duality suggests that while green logistics holds long-term promise, its short-term inflationary risks require careful policy design. 3. Methodology and Research Design 1. Introduction This study employs a quantitative research approach to analyze the relationships between renewable energy adoption, logistics costs, and food price inflation. Various statistical techniques, including descriptive statistics, correlation analysis, time-series analysis, regression modeling, and Granger causality tests, are applied to explore patterns, trends, and causality within the dataset. 2. Research Design The study follows an empirical research design utilizing secondary data from government reports, financial market databases, and energy sector publications. The design is structured into the following analytical stages: 2.1 Data Collection and Variables Data were obtained from publicly available economic and energy sector sources. The key variables analyzed include: Dependent Variables: Logistics Cost (measured in transport cost indices) Food Consumer Price Index (CPI) Independent Variables: Renewable Energy Adoption (% of total energy use) Oil Prices (USD per barrel) Government Policies (Renewable Energy Subsidy Index) GDP Growth Rate (%) Exchange Rate (USD to local currency) 2.2 Statistical Techniques The study employs multiple statistical methodologies to explore relationships between these variables: 2.2.1 Descriptive Statistics Measures of central tendency (mean, median) Measures of dispersion (standard deviation, variance) Distribution analysis (skewness, kurtosis) These analyses provide foundational insights into data behavior before conducting inferential analyses. 2.2.2 Correlation Analysis Pearson Correlation Coefficient : Assesses the strength and direction of linear relationships between variables. A correlation matrix is constructed to examine relationships such as: Renewable Energy Adoption vs. Logistics Costs Renewable Energy Adoption vs. Food CPI Logistics Costs vs. Food CPI 2.2.3 Time-Series Analysis ARIMA (Autoregressive Integrated Moving Average) Model : Used to forecast transport costs by examining past values and trends. VAR (Vector Autoregression) Model : Captures the dynamic interdependencies among renewable adoption, logistics costs, and food CPI over time. 2.2.4 Regression Analysis Multiple Linear Regression (MLR) : Quantifies the effect of independent variables on transport costs and food CPI. The regression models take the form: Logistics Cost = f(Renewable Adoption, Oil Price, Government Policy, GDP Growth, Exchange Rate) Food CPI = f(Renewable Adoption, Oil Price, Government Policy, GDP Growth, Exchange Rate) Diagnostic tests such as multicollinearity (Variance Inflation Factor), heteroscedasticity (Breusch-Pagan test), and autocorrelation (Durbin-Watson test) are conducted to ensure model validity. 2.2.5 Granger Causality Tests Determines whether changes in renewable energy adoption predict changes in logistics costs and food CPI. Hypotheses tested: H1: Renewable Energy Adoption Granger-causes Transport Costs. H2: Renewable Energy Adoption Granger-causes Food CPI. This methodological framework enables a comprehensive examination of the impact of renewable energy adoption on economic variables. The integration of multiple statistical techniques ensures robustness in the analysis, providing valuable insights for policymakers and businesses aiming to transition to sustainable energy solutions. Findings and Analysis 4.1. Descriptive Statistical Analysis Descriptive statistics provide a foundational understanding of key variables, summarizing data through measures such as mean, standard deviation, and distribution analysis. The analysis focuses on three main aspects: renewable energy adoption, transport costs, and food price inflation. 4.2 Key Statistical Measures Mean Analysis: Renewable energy adoption rate averaged 3.64% , indicating relatively low but consistent adoption. Transport costs were found to be moderate and stable over time. Food Consumer Price Index (CPI) showed a gradual increase , influenced by transport costs and economic conditions. Standard Deviation Analysis: Renewable energy adoption showed moderate variation (SD = 2.59%) , indicating occasional fluctuations. Transport costs exhibited significant fluctuations , influenced by factors like fuel prices and supply chain disruptions. Food CPI had higher variability , signifying sensitivity to economic and logistical shifts. Distribution Analysis: Renewable adoption exhibited right-skewed distribution , indicating occasional policy-driven spikes. Transport costs showed moderate skewness , reflecting market disruptions. Food CPI was right-skewed , implying periodic price surges. 4.3 key takeaway The mean values suggest general stability in the analyzed variables. Moderate standard deviations in renewable adoption and transport costs indicate periodic shifts . Skewed distributions in food CPI and transport costs suggest external shocks impact these variables . 4.2. Correlation Analysis 4.2.1 Overview Pearson correlation analysis was conducted to assess relationships between renewable energy adoption, logistics costs, and food price inflation. 4.2.2 Key Findings Renewable Energy Adoption vs. Transport Costs : Weak negative correlation (-0.1233) suggests that renewable energy adoption does not significantly reduce logistics costs in the short term. Renewable Energy Adoption vs. Food CPI : Strong positive correlation (0.6934) indicates that increasing renewable energy adoption is associated with rising food prices. Transport Costs vs. Food CPI : Moderate negative correlation (-0.3138) suggests thatrising transport costs might encourage supply chain optimizations that stabilize food prices . 4.2.3 Implications Investment in Green Logistics : While renewable adoption does not immediately lower transport costs, sustained investment in electric/hydrogen-based transport could yield long-term benefits. Food Price Stability : Given the strong positive correlation between renewable adoption and food CPI, targeted policies are needed to mitigate food price inflation. Balanced Transition Approach : A gradual transition to renewable energy in logistics and food supply chains is necessary to balance sustainability with economic stability. 4.3. Time-Series Analysis 4.3.1 ARIMA Model for Transport Costs ARIMA (1,0,0) results indicate that transport costs depend significantly on their past values (AR coefficient = 0.9089, p < 0.001) . Diagnostic tests reveal some autocorrelation and non-normality , suggesting additional external factors influencing transport costs. Policy implication: Any external shock (e.g., fuel price hikes) will have prolonged effects, requiring long-term stabilization policies. 4.3.2 Vector Autoregression (VAR) Model Findings Renewable adoption significantly impacts transport costs (Coefficient = 3.78548, p = 0.007) , indicating initial cost increases during transition. Food CPI shows a weaker response to renewable adoption and transport costs , suggesting other macroeconomic influences. Transport costs exhibit strong autoregressive behavior , meaning past values significantly predict future costs. Policy implication: Investments in renewable energy should be accompanied by policies that offset short-term cost increases. 4.3.3 Policy Implications Short-term cost increases due to renewable adoption require targeted subsidies . Food price responses to transport costs are weaker than expected , indicating the role of broader economic factors. Long-term investments in green logistics are needed to stabilize costs . 4. Regression Analysis 4.1 Multiple Linear Regression Findings Logistics Cost Model : Renewable adoption positively impacts logistics costs ( β = 2.789, p < 0.01 ), suggesting short-term cost increases. Government policies help mitigate logistics costs ( β = -1.234, p < 0.05 ). GDP growth and oil prices also influence transport costs. Food CPI Model : Renewable adoption positively impacts food prices ( β = 1.432, p < 0.05 ). Oil price increases lead to higher food inflation. Favorable government policies reduce food price inflation ( β = -0.934, p < 0.05 ). 4.2 Key Insights Renewable adoption leads to short-term cost increases in logistics and food prices . Government interventions play a key role in stabilizing economic impacts . Macroeconomic stability (exchange rates, GDP growth) significantly influences cost structures . Granger Causality Analysis 5.1 Key Findings Renewable Energy Adoption → Transport Costs : Significant causality , confirming that renewable energy adoption influences transport costs over time. Renewable Energy Adoption → Food CPI : No significant causality , indicating that other economic factors have a stronger role in food price movements. 5.2 Policy Implications Investment in Green Logistics : Long-term subsidies for electric and hydrogen-powered transportation can reduce initial cost burdens. Managing Food Price Inflation : Policies should balance renewable adoption incentives with measures that stabilize food production costs. Balancing Short-Term and Long-Term Costs : The transition to renewable energy involves short-term cost increases, requiring strategic interventions. The findings confirm that renewable energy adoption influences transport costs and food prices, with short-term cost increases but potential long-term benefits . Policy interventions, such as subsidies and infrastructure investments, are essential for mitigating economic disruptions during the transition. 4. Discussions Renewable Energy Adoption and Logistics Costs: Short-Term Pressures vs. Long-Term Benefits The findings of this study confirm that renewable energy adoption in logistics is associated with initial cost increases, largely due to infrastructure investments in EVs, charging stations, and renewable-powered cold-chain systems. Similar results are reported in international and Indian contexts, where the upfront costs of green transitions temporarily raise logistics expenses (Havenga, 2010; Mohapatra et al., 2021; Jagtap et al., 2020). For instance, case studies on India’s renewable energy integration into transport highlight that solar-powered EV systems and biofuels can only achieve cost competitiveness after sustained adoption (Barton & Thomson, 2021; Basavaraj et al., 2012). Nevertheless, long-term benefits such as reduced fossil fuel dependency, improved energy security, and resilience against oil price shocks are widely recognized (Varghese, 2017; Sultan et al., 2020; Kaushik & Shastri, 2024). International experiences further suggest that renewable-powered cold storage, green packaging, and sustainable logistics automation can stabilize costs over time, provided policy support is in place (Lingaitienė & Burinskienė, 2024; Meneghetti et al., 2018; Tsolakis et al., 2023). Thus, governments should complement renewable adoption with targeted subsidies, tax incentives, and infrastructure development to minimize short-term disruptions (Desai, 2021; Lu et al., 2020). Logistics Costs and Food Price Inflation: A Complex Relationship Contrary to conventional theory, the correlation analysis in this study showed a moderate negative relationship between logistics costs and food price inflation. This suggests that rising transport costs may encourage efficiency gains, such as route optimization, local sourcing, and supply chain digitization (Kharaishvili & Gechbaia, 2023; Rathore et al., 2021). Previous studies also indicate that while logistics costs significantly influence food inflation, broader structural drivers including climate shocks, policy inefficiencies, and global market volatility often overshadow this relationship (Mishra & Roy, 2012; Bhattacharya, 2016; Chandrasekhar, 2012; Dash & Lugauer, 2024). Food inflation in India has historically been shaped by systemic inefficiencies, including supply chain distortions (Bhattacharya, 2016), APMC market rigidities (Neha Tomar, 2013), and cold-chain bottlenecks (Maheshwar & Chanakwa, 2006). Disruptions during the COVID-19 pandemic further underscored the fragility of Indian food supply chains, amplifying the impact of logistics inefficiencies on food inflation (Reardon et al., 2020; Sudan & Taggar, 2021). Comparative evidence also shows that international food and fuel price shocks are quickly transmitted to domestic inflation, exacerbating volatility in India (Sultan et al., 2020; Zhang et al., 2024). These findings indicate that while logistics costs remain a key determinant of food prices, effective policy interventions must address both micro-level inefficiencies (cold storage, intermediaries, transport resilience) and macro-level shocks (oil prices, global food markets, climate risks). Renewable Energy Adoption and Food Price Inflation: Inflationary Risks During Transition The study confirms a strong positive correlation between renewable energy adoption and food price inflation, reinforcing concerns that green logistics integration may exert inflationary pressures in the short run. This aligns with earlier evidence that renewable-powered cold storage, biofuel logistics, and EV-based food transport require significant capital investment, often passed on to consumers (Mogale et al., 2020; Gawusu, 2024; Gorjian et al., 2022). Similarly, empirical analyses show that international oil price volatility, monetary policies, and renewable adoption costs together shape food price movements in emerging economies (Anand et al., 2014; Mishra & Agarwal, 2021; Dar & Asif, 2023). However, long-term benefits remain compelling. Renewable adoption can shield economies from fossil fuel volatility, promote food supply chain resilience, and stabilize transport costs once infrastructure matures (Bekun, 2022; Abid & Saqlain, 2023; Umar & Wilson, 2024). Moreover, research indicates that green packaging innovations and circular economy practices can reduce the inflationary burden associated with renewable adoption (Panghal et al., 2024; Lingaitienė & Burinskienė, 2024). Thus, the inflationary risks of renewable logistics transitions should be viewed as temporary, requiring careful policy sequencing rather than as deterrents to sustainability. Policy Implications Short-Term Subsidies and Incentives; To counter the immediate cost burden of renewable adoption, governments should provide targeted subsidies, concessional loans, and tax rebates to logistics firms investing in EVs, cold-chain renewables, and green warehousing (Lu et al., 2020; Desai, 2021). Strengthening Food Supply Chains; Addressing inflation requires interventions beyond logistics costs such as investments in cold storage, market reforms, and digital logistics systems (Maheshwar & Chanakwa, 2006; Jagtap et al., 2020). Strengthening postharvest infrastructure can reduce losses and moderate price pressures. Mitigating Global Shock Transmission; Since global oil and food commodity markets strongly influence Indian inflation, macroeconomic buffers such as foreign exchange stabilization and trade diversification policies are critical (Varghese, 2017; Sultan et al., 2020; Zhang et al., 2024). Encouraging Circular Economy and Green Packaging; Circular economy innovations including renewable packaging, IoT-based logistics, and energy-efficient food storage can offset the inflationary effects of renewable adoption (Lingaitienė & Burinskienė, 2024; Singh & Roy, 2020). Gradual and Phased Transition; A sudden shift to renewables risks escalating logistics costs and food prices. A gradual, phased transition, supported by policies on trade, energy, and food inflation, is necessary to balance sustainability and economic stability (Kiesecker et al., 2019; Sudan & Taggar, 2021). 5. Conclusions This study examined the relationship between renewable energy adoption, logistics costs, and food price inflation using a combination of descriptive statistics, correlation analysis, time-series modelling, regression analysis, and Granger causality tests. The findings provide valuable insights into the economic implications of transitioning to renewable energy in logistics and its potential impact on food prices. This conclusion summarizes the key takeaways and their policy implications. 1. Key Findings Renewable Energy Adoption Increases Logistics Costs in the Short Term: Regression analysis showed that a 1% increase in renewable energy adoption led to a 2.79% increase in logistics costs. The time-series ARIMA model confirmed the persistence of logistics cost fluctuations over time. The increase in costs is primarily due to the high initial investment in green transport infrastructure (e.g., electric vehicles, charging stations). The Relationship Between Logistics Costs and Food Price Inflation is Complex: A moderate negative correlation (-0.3138) was observed between logistics costs and food price inflation, suggesting that rising transport costs might encourage supply chain efficiencies that stabilize food prices. Regression results showed that while logistics costs influence food prices, their effect is overshadowed by macroeconomic factors such as oil price volatility and government interventions. Renewable Energy Adoption is Positively Correlated with Food Price Inflation: A strong positive correlation (0.6934) was found between renewable adoption and food CPI, indicating that as renewable energy adoption increases, food prices also rise. Regression analysis confirmed that a 1% increase in renewable adoption corresponds to a 1.43% increase in food price inflation. The cost implications of transitioning to renewable-powered food logistics and processing appear to drive this inflationary effect. Renewable Energy Adoption Granger-Causes Logistics Costs but Not Food Prices: Granger causality tests confirmed that changes in renewable energy adoption significantly influence logistics costs over time. However, no significant causality was found between renewable energy adoption and food CPI, suggesting that external macroeconomic factors play a larger role in food price fluctuations. 2. Policy Implications Financial Support for Green Logistics: Short-term cost increases due to renewable energy adoption can be mitigated through subsidies, tax incentives, and low-interest financing for green logistics investments. Government policies should support logistics firms in adopting electric or hydrogen-powered fleets to ensure long-term cost reductions. Targeted Measures to Control Food Price Inflation: Since renewable energy adoption is linked to rising food prices, policymakers should implement food price stabilization measures, such as agricultural subsidies and supply chain optimization programs. Encouraging localized food production and renewable-powered cold storage facilities can reduce cost pressures on food supply chains. Investment in Renewable Infrastructure to Reduce Costs: Developing large-scale renewable energy infrastructure, such as nationwide charging networks and biofuel production facilities, can help lower logistics costs over time. Coordinated investment between the government and private sector can accelerate cost reductions in green logistics. Gradual Transition to Minimize Economic Disruptions: A phased approach to integrating renewable energy into logistics and food supply chains can prevent sharp cost increases. Policymakers should adopt transition frameworks that balance sustainability goals with economic stability. Future Research on External Economic Factors: The weak causality between transport costs and food inflation suggests that other macroeconomic factors (e.g., oil prices, currency fluctuations, and trade policies) influence food prices. Further studies should integrate additional variables to refine the understanding of the renewable energy-food price nexus. 3. Contribution to Research and Future Directions This study contributes to the growing body of literature on sustainable logistics and food supply chains by providing empirical evidence on the economic trade-offs of renewable energy adoption. The findings highlight the need for a balanced approach to sustainability, where environmental benefits are achieved without causing significant economic disruptions. Future research should: Investigate long-term cost reductions from renewable energy adoption in logistics. Explore the role of government policies in mitigating initial cost increases. Assess regional differences in renewable adoption and its economic impact on food prices. Apply machine learning and econometric models to predict future trends in green logistics costs and food inflation. The transition to renewable energy in logistics presents both challenges and opportunities. While short-term cost pressures exist, strategic policy interventions can ensure that long-term economic and environmental benefits outweigh initial disruptions. By fostering sustainable logistics, governments and businesses can achieve a greener, more resilient supply chain while safeguarding economic stability. This research underscores the importance of integrating economic, policy, and technological considerations in renewable energy transitions. A well-planned shift toward sustainable logistics can pave the way for a future where environmental responsibility and economic efficiency coexist. Declarations 1. Data Availability Statement The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. 2. Ethics and Guidelines This study followed all relevant ethical guidelines for research and publication. The research did not involve human participants or animals, and therefore did not require formal ethical approval. 3. Consent to Participate Not applicable. The study did not involve human participants requiring informed consent. 4. Consent to Publish Not applicable. The manuscript does not contain data from any individual person, and therefore consent for publication was not required. 5. Ethics, Consent to Participate, and Consent to Publish This study does not involve human participants, patient records, or clinical samples. The research is based on economic modeling, secondary data analysis from publicly available sources, and conceptual frameworks related to renewable energy adoption in logistics. Therefore: Ethics approval and consent to participate: Not applicable, as no human subjects or clinical interventions were involved in the study. Consent for publication: Not applicable, as the manuscript does not contain any identifiable personal data, images, or case details of individuals. Clinical Trial Registration The study is not a clinical trial and does not involve any form of medical, clinical, or patient-related interventions. The research exclusively focuses on analyzing the economic implications of renewable energy adoption in logistics, particularly its effect on transportation costs and food price inflation, using secondary data sources and conceptual modeling. Hence, trial registration details (registry name, trial number, and date) are not applicable. Funding Declarations This research received no external funding. Author Contribution Janardhana Anjanappa: Conceptualized the research idea, designed the study framework, supervised the research process, and contributed to the interpretation of results and policy implications. Drafted and critically revised the manuscript for intellectual content.Shridhar M. Samant: Collected and curated secondary data, performed statistical and econometric analyses (descriptive statistics, correlation, ARIMA, VAR, regression, Granger causality), and contributed to the preparation of results and discussion sections. References Sharma RK, Dharni K, Smagh A, Vashisht P. Relationship between logistics cost and relative firm efficiency in Indian food processing sector. J Econ Manage Trade. 2021;27(1):42–52. Kumar S, Bharj RS. Solar hybrid e-cargo rickshaw for urban transportation demand in India. Transp Res Procedia. 2020;48:1998–2005. Roy S, Mohanty RP. Green logistics operations and its impact on supply chain sustainability: An empirical study. 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K., Narasimhan, S. R., Joshi, M., Cochran, J., Palchak, D., Ehlen, A.,… Deshmukh, R. (2018, May). Analysis of strategies for integrating 175 GW of renewable energy in India. In 2018 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia)(pp. 1159–1164). IEEE. Reardon T, Mishra A, Nuthalapati CS, Bellemare MF, Zilberman D. COVID-19’s disruption of India’s transformed food supply chains. Economic Political Wkly. 2020;55(18):18–22. Maheshwar C, Chanakwa T. (2006, August). Postharvest losses due to gaps in cold chain in India-a solution. In IV International Conference on Managing Quality in Chains-The Integrated View on Fruits and Vegetables Quality 712 (pp. 777–784). Jain S, Shrimali G. Impact of renewable electricity on utility finances: Assessing merit order effect for an Indian utility. Energy Policy. 2022;168:113092. Chandrasekhar CP. Food price levels and volatility: Sources, impact and implications. IDS Bull. 2012;43:74–83. Hussain MM, Pal S, Villanthenkodath MA. Towards sustainable development: The impact of transport infrastructure expenditure on the ecological footprint in India. Innov Green Dev. 2023;2(2):100037. Lu T, Sherman P, Chen X, Chen S, Lu X, McElroy M. India’s potential for integrating solar and on-and offshore wind power into its energy system. Nat Commun. 2020;11(1):4750. Amir M, Ansari AR, Khan MA, Kaur J, Bakhsh FI, Khosla A. Feasibility Analysis of Solar Energy and Role of Lithium-Ion Battery Reserved in Electric Vehicle Market: A Path Towards Green Transportation. Strategic Planning for Energy and the Environment; 2024. pp. 545–68. Gunatilake HM, Roland-Holst D, Sugiyarto G, Baka J. (2011). Energy security and economics of Indian biofuel strategy in a global context. Asian Development Bank Economics Working Paper Series, (269). Khan SAR. (2019). The Effect of Green logistics on Economic growth, Social and Environmental sustainability: An Empirical study of Developing countries in Asia. Preprints. Shahani R, Taneja A, Das B. Dynamic Interaction Between Food and Fuel Markets in India: Has India Joined the Global Race? Green Low-Carbon Econ. 2024;2(4):287–98. Dasgupta D, Dubey RN, Satish R. (2011). Domestic wheat price formation and food inflation in India: International prices, domestic drivers (stocks, weather, public policy), and the efficacy of public policy interventions in wheat markets (No. id: 4291). Mukherjee A, Satija D, Sinha S, Sarma AP. (2019). Food imports in India: prospects, issues and way forward. J Economic Sci Res, 2(3). Moorthy V, Kolhar S. Rising food inflation and India's monetary policy. Indian Growth Dev Rev. 2011;4(1):73–94. Havenga J. Logistics costs in South Africa–The case for macroeconomic measurement. South Afr J Econ. 2010;78(4):460–76. Sonna T, Joshi H, Sebastin A, Sharma U. (2014). Analytics of food inflation in India (No. id: 6174). Mishra A, Kumar DS, Suresh DJ. (2013). Optimization of Supply Chain Logistics Cost. International Journal of Management (IJM), 4(1), 130–135. Rathore R, Thakkar JJ, Jha JK. Impact of risks in foodgrains transportation system: a system dynamics approach. Int J Prod Res. 2021;59(6):1814–33. Murshed M, Mahmood H, Alkhateeb TTY, Banerjee S. Calibrating the impacts of regional trade integration and renewable energy transition on the sustainability of international inbound tourism demand in South Asia. Sustainability. 2020;12(20):8341. Kaushik K, Shastri S. Oil prices, renewable energy consumption and trade balance nexus: empirical evidence from Indian economy. Sustain Acc Manage Policy J. 2024;15(3):731–51. Fulzele V, Shankar R, Choudhary D. A model for the selection of transportation modes in the context of sustainable freight transportation. Industrial Manage Data Syst. 2019;119(8):1764–84. Dubey B, Agrawal S, Sharma AK. India’s renewable energy portfolio: an investigation of the untapped potential of RE, policies, and incentives favoring energy security in the country. Energies. 2023;16(14):5491. Meneghetti A, Dal Magro F, Simeoni P. Fostering renewables into the cold chain: how photovoltaics affect design and performance of refrigerated automated warehouses. Energies. 2018;11(5):1029. Rehman FU, Khan D. The determinants of food price inflation in Pakistan: An econometric analysis. Adv Econ Bus. 2015;3(12):571–6. Saravanan V, Venkatachalam KM, Arumugam M, Borelessa MAK, Hemapala KT M. U. Impact of renewable energy in Indian electric power system. Int J Adv Appl Sci. 2021;10(4):297–309. Goswami R. The Food Industry in India and Its Logic. Economic and Political Weekly; 2010. pp. 15–8. Malhotra A, Maloo M. (2017). Understanding food inflation in India: A Machine Learning approach. arXiv preprint arXiv:1701.08789. Gawusu S. (2024). Impact of renewable energy integration on commodity markets. Available at SSRN 4682719. Umar M, Wilson MM. (2024). Inherent and adaptive resilience of logistics operations in food supply chains. J Bus Logistics, 45(1), e12362. Nayak P, Jena PK. Inflationary pressures on India’s domestic food prices: unravelling the impact of global macroeconomic factors. South Asian Journal of Business Studies; 2024. Abid M, Saqlain M. Decision-making for the bakery product transportation using linear programming. Spectr Eng Manage Sci. 2023;1(1):1–12. Dieaconescu RI, Belu MG, Gheorghe M. (2022). Impact of Oil Price Evolution on Logistics Industry. The Romanian Economic Journal, (84). Tsolakis N, Harrington TS, Srai JS. Leveraging automation and data-driven logistics for sustainable farming of high-value crops in emerging economies. Smart agricultural Technol. 2023;4:100139. Maiyar LM, Thakkar JJ. Robust optimisation of sustainable food grain transportation with uncertain supply and intentional disruptions. Int J Prod Res. 2020;58(18):5651–75. Zhang L, Padhan H, Singh SK, Gupta M. The impact of renewable energy on inflation in G7 economies: Evidence from artificial neural networks and machine learning methods. Energy Econ. 2024;136:107718. Kumar A, Kushwaha GS. Food supply chain management sustainability: a review. Int Journal’s Res J Sci IT Manage. 2014;3(10):30–42. Additional Declarations No competing interests reported. Supplementary Files Dataset.docx Annex.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Introduction","content":"\u003cp\u003eThe global logistics sector, a critical artery of international commerce and economic development, is undergoing a profound transformation driven by the imperative of sustainability. Rising concerns over climate change, energy security, and environmental degradation have made the integration of renewable energy sources into logistics operations a pivotal strategy for reducing the carbon footprint of supply chains (Khan, 2019; Khan et al., 2019). This transition encompasses the adoption of electric vehicles (EVs), biofuels, solar-powered infrastructure, and smart technologies, and is increasingly recognized not merely as an environmental obligation but as a strategic component of modern supply chain management (Roy \u0026amp; Mohanty, 2024; Barton \u0026amp; Thomson, 2021; Alagarsamy et al., 2021). In emerging economies such as India, where logistics costs constitute nearly 14% of GDP, the shift toward renewable-powered logistics presents both opportunities and risks (Sharma et al., 2021; Havenga, 2010; Basavaraj et al., 2012).\u003c/p\u003e\n\u003cp\u003eHowever, the pathway to a green logistics paradigm is fraught with complex trade-offs. The initial capital outlay required for renewable energy infrastructure including EV charging stations, biofuel processing plants, and cold-chain facilities often results in short-term escalations in transportation costs (Singh et al., 2024; Sobha et al., 2023; Meneghetti et al., 2018). These increased logistics costs can ripple across the economy, particularly through food supply chains, where transportation is a major cost component (De \u0026amp; Rout, 2008; Fulzele et al., 2019). Empirical evidence suggests that fluctuations in transportation costs significantly influence food inflation, especially in developing countries where supply chains are vulnerable to inefficiencies and shocks (Shively \u0026amp; Thapa, 2017; Mishra \u0026amp; Roy, 2012; Bhattacharya, 2016; Bhattacharya \u0026amp; Sen Gupta, 2018). Moreover, food price volatility in India has often been linked to logistics bottlenecks, oil price shocks, and systemic inefficiencies in supply chain management (Varghese, 2017; Lahiri \u0026amp; Ghosh, 2014; Gupta \u0026amp; Siddiqui, 2014; Anand et al., 2014; Moorthy \u0026amp; Kolhar, 2011; Goswami, 2010).\u003c/p\u003e\n\u003cp\u003eThe challenge is further amplified by broader macroeconomic and environmental dynamics. Research indicates that food inflation in India is shaped not only by logistics inefficiencies but also by climate variability, international price transmission, and policy constraints (Mitra \u0026amp; Chattopadhyay, 2017; Chandrasekhar, 2012; Shahani et al., 2024; Mishra \u0026amp; Agarwal, 2021; Dash \u0026amp; Lugauer, 2024). Climate-induced risks, such as monsoon variability, contribute to food price volatility (Birthal et al., 2019), while cold-chain gaps result in significant postharvest losses (Maheshwar \u0026amp; Chanakwa, 2006). Furthermore, renewable energy integration itself is influenced by land use conflicts, trade competitiveness, and financial barriers (Kiesecker et al., 2019; Desai, 2021; Yu et al., 2022; Murshed et al., 2020). Studies also highlight that the introduction of green packaging and circular economy practices in logistics can help offset rising costs, though adoption barriers remain (Lingaitienė \u0026amp; Burinskienė, 2024; Panghal et al., 2024; Kashem et al., 2024).\u003c/p\u003e\n\u003cp\u003eIn the Indian context, supply chain disruptions such as those caused by COVID-19 highlighted the fragility of food logistics, reinforcing the link between transport costs and inflationary pressures (Reardon et al., 2020; Sudan \u0026amp; Taggar, 2021). Oil price fluctuations, global macroeconomic conditions, and monetary policy interventions further complicate the nexus between logistics costs and food inflation (Sultan et al., 2020; Dar \u0026amp; Asif, 2023; Kaushik \u0026amp; Shastri, 2024; Nayak \u0026amp; Jena, 2024). At the same time, renewable adoption in logistics is associated with inflationary risks during transition phases, as green technologies and infrastructure investments tend to increase input costs (Mogale et al., 2020; Gao et al., 2019; Gawusu, 2024; Zhang et al., 2024). Yet, long-term benefits such as reduced fossil fuel dependency, improved efficiency, and stabilized energy costs remain compelling (Bekun, 2022; Gorjian et al., 2022; Lu et al., 2020; Amir et al., 2024).\u003c/p\u003e\n\u003cp\u003eDespite this growing body of literature, a significant gap persists in understanding the integrated dynamics between renewable energy adoption in logistics, its impact on transportation costs, and the resultant effect on food price inflation (Gupta \u0026amp; Dhar, 2022; Kar \u0026amp; Datta, 2020). While some studies examine logistics costs (Jagtap et al., 2020; Mohapatra et al., 2021) or food inflation drivers (Nair \u0026amp; Eapen, 2013; Sonna et al., 2014; Malhotra \u0026amp; Maloo, 2017), few provide a comprehensive empirical analysis of their interconnections. Addressing this gap is particularly crucial for developing economies such as India, where food security, energy transitions, and inflation stability intersect (Anand, 2014; Gunatilake et al., 2011; Mukherjee et al., 2019).\u003c/p\u003e\n\u003cp\u003eTherefore, this study seeks to investigate the economic implications of renewable energy adoption in logistics, focusing specifically on its dual impact on transportation costs and food price inflation. By employing a combination of descriptive statistics, time-series modelling, regression analysis, and Granger causality tests, the research aims to disentangle these relationships and provide policy-relevant insights. In doing so, the paper contributes to debates on how economies can balance environmental sustainability with inflation stability in an era of energy transition.\u003c/p\u003e"},{"header":"2.\tLiterature Review","content":"\u003cp\u003e\u003cstrong\u003eRenewable Energy Adoption in Logistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe logistics sector in India is steadily transitioning toward renewable energy sources such as solar, wind, and biofuels, with the objective of reducing dependency on fossil fuels and meeting sustainability goals (Roy \u0026amp; Mohanty, 2024; Basavaraj et al., 2012). Adoption strategies include electric and hybrid vehicles, biofuel-powered fleets, and renewable-powered cold-chain systems (Kumar \u0026amp; Bharj, 2020; Kashem et al., 2024; Amir et al., 2024). While these approaches promise long-term cost savings and emissions reductions, they often require significant upfront investments in vehicles, infrastructure, and charging networks (Singh et al., 2024; Sobha et al., 2023; Mohapatra et al., 2021).\u003c/p\u003e\n\u003cp\u003eGlobal studies show that energy storage, biofuel strategies, and renewable-powered warehousing can play a critical role in accelerating this transition (Barton \u0026amp; Thomson, 2021; Gunatilake et al., 2011; Meneghetti et al., 2018). Moreover, consumer demand for sustainable products and green logistics practices further drives adoption, with studies linking green consumption values to the diffusion of renewable-powered supply chains (Alagarsamy et al., 2021; Panghal et al., 2024). However, barriers such as limited policy support, technological readiness, and infrastructure gaps constrain large-scale adoption, particularly in developing economies (Khanna, 2022; Desai, 2021; Kiesecker et al., 2019).\u003c/p\u003e\n\u003cp\u003eDespite these challenges, the literature identifies opportunities for integrating renewables into logistics networks. Circular economy approaches, sustainable packaging, and IoT-enabled logistics planning can support efficiency gains and cost savings in renewable-powered supply chains (Lingaitienė \u0026amp; Burinskienė, 2024; Singh \u0026amp; Roy, 2020; Helo \u0026amp; Luomala, 2011). Nonetheless, questions remain on whether such interventions can offset the short-term cost pressures associated with energy transitions in logistics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact on Transportation Costs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cost implications of renewable energy adoption in logistics are complex and often context-dependent. While renewable-powered fleets reduce dependency on fossil fuels in the long run, their deployment requires capital-intensive infrastructure, which may initially drive transportation costs higher (Sharma et al., 2021; Jagtap et al., 2020). Research from India highlights how logistics sprawl and inefficiencies add to transportation costs, especially in urban food supply chains (Mohapatra et al., 2021; Mogale et al., 2020).\u003c/p\u003e\n\u003cp\u003eEmpirical studies demonstrate that logistics cost structures are shaped by fuel prices, technological advances, and infrastructure availability. Oil price shocks in particular have strong inflationary effects, contributing to cost escalation in transport operations (Varghese, 2017; Sultan et al., 2020; Kaushik \u0026amp; Shastri, 2024). Cost measurement studies further suggest that logistics costs can reach up to 14% of GDP in developing economies, significantly constraining competitiveness (Havenga, 2010; De \u0026amp; Rout, 2008).\u003c/p\u003e\n\u003cp\u003eInternational experiences indicate that cost optimization through renewable adoption can be achieved over time by deploying solar-powered cold chains, optimized distribution systems, and automation technologies (Meneghetti et al., 2018; Dieaconescu et al., 2022; Tsolakis et al., 2023). Dynamic models confirm that renewable energy investments are sensitive to macroeconomic conditions, particularly oil prices, exchange rates, and trade integration patterns (Gao et al., 2019; Murshed et al., 2020; Yu et al., 2022). For India, this suggests that transport costs during the transition phase will remain volatile unless supported by robust subsidies and infrastructure investments (Saravanan et al., 2021; Lu et al., 2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLink between Transportation Costs and Food Price Inflation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between logistics costs and food inflation is well established but highly nuanced. Rising transportation costs can increase food prices by amplifying the costs embedded in agricultural supply chains (Shively \u0026amp; Thapa, 2017; Bhattacharya \u0026amp; Sen Gupta, 2018). However, in some cases, higher costs force supply chain actors to innovate, optimize routes, and reduce inefficiencies, leading to mixed outcomes (Kharaishvili \u0026amp; Gechbaia, 2023; Rathore et al., 2021).\u003c/p\u003e\n\u003cp\u003eIndian evidence suggests that food price inflation is influenced by multiple structural drivers, including fuel price shocks, weak storage systems, and global market volatility (Mishra \u0026amp; Roy, 2012; Bhattacharya, 2016; Goswami, 2010; Chandrasekhar, 2012; Birthal et al., 2019). Policy failures and market rigidities, such as those stemming from the APMC Act, have further contributed to persistent inflationary pressures in food markets (Neha Tomar, 2013; Nair \u0026amp; Eapen, 2013). Supply chain disruptions during COVID-19 also underscored how vulnerable India\u0026rsquo;s food logistics networks remain to external shocks (Reardon et al., 2020; Sudan \u0026amp; Taggar, 2021).\u003c/p\u003e\n\u003cp\u003eBeyond domestic dynamics, studies indicate that international food and fuel markets exert significant pressure on Indian food prices. Oil price volatility and global commodity price movements have consistently transmitted inflationary effects into the Indian economy (Sultan et al., 2020; Dash \u0026amp; Lugauer, 2024; Zhang et al., 2024). Structural models confirm that food inflation in India is shaped by monetary policies, climate shocks, and trade integration (Anand et al., 2014; Mishra \u0026amp; Agarwal, 2021; Dar \u0026amp; Asif, 2023). Cold chain inefficiencies, which result in postharvest losses of perishable goods, also exacerbate inflation (Maheshwar \u0026amp; Chanakwa, 2006).\u003c/p\u003e\n\u003cp\u003eIn the broader sustainability literature, researchers emphasize that renewable adoption in food logistics may itself act as a double-edged sword. On one hand, renewable-powered cold storage and transport can stabilize food supply chains over the long run (Gorjian et al., 2022; Abid \u0026amp; Saqlain, 2023; Umar \u0026amp; Wilson, 2024). On the other hand, initial cost surges in renewable infrastructure can exacerbate food price inflation during transition phases (Gawusu, 2024; Malhotra \u0026amp; Maloo, 2017). This duality suggests that while green logistics holds long-term promise, its short-term inflationary risks require careful policy design.\u003c/p\u003e"},{"header":"3.\tMethodology and Research Design","content":"\u003ch2\u003e1. Introduction\u003c/h2\u003e\n\u003cp\u003eThis study employs a quantitative research approach to analyze the relationships between renewable energy adoption, logistics costs, and food price inflation. Various statistical techniques, including descriptive statistics, correlation analysis, time-series analysis, regression modeling, and Granger causality tests, are applied to explore patterns, trends, and causality within the dataset.\u003c/p\u003e\n\u003ch2\u003e2. Research Design\u003c/h2\u003e\n\u003cp\u003eThe study follows an empirical research design utilizing secondary data from government reports, financial market databases, and energy sector publications. The design is structured into the following analytical stages:\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e2.1 Data Collection and Variables\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eData were obtained from publicly available economic and energy sector sources. The key variables analyzed include:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eDependent Variables:\u003c/strong\u003e\u0026nbsp;\u003cul type=\"circle\"\u003e\n \u003cli\u003eLogistics Cost (measured in transport cost indices)\u003c/li\u003e\n \u003cli\u003eFood Consumer Price Index (CPI)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIndependent Variables:\u003c/strong\u003e\u0026nbsp;\u003cul type=\"circle\"\u003e\n \u003cli\u003eRenewable Energy Adoption (% of total energy use)\u003c/li\u003e\n \u003cli\u003eOil Prices (USD per barrel)\u003c/li\u003e\n \u003cli\u003eGovernment Policies (Renewable Energy Subsidy Index)\u003c/li\u003e\n \u003cli\u003eGDP Growth Rate (%)\u003c/li\u003e\n \u003cli\u003eExchange Rate (USD to local currency)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003cstrong\u003e2.2 Statistical Techniques\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe study employs multiple statistical methodologies to explore relationships between these variables:\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e2.2.1 Descriptive Statistics\u003c/strong\u003e\u003c/h4\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eMeasures of central tendency (mean, median)\u003c/li\u003e\n \u003cli\u003eMeasures of dispersion (standard deviation, variance)\u003c/li\u003e\n \u003cli\u003eDistribution analysis (skewness, kurtosis) These analyses provide foundational insights into data behavior before conducting inferential analyses.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003cstrong\u003e2.2.2 Correlation Analysis\u003c/strong\u003e\u003c/h4\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003ePearson Correlation Coefficient\u003c/strong\u003e: Assesses the strength and direction of linear relationships between variables.\u003c/li\u003e\n \u003cli\u003eA correlation matrix is constructed to examine relationships such as:\u0026nbsp;\u003col\u003e\n \u003cli\u003eRenewable Energy Adoption vs. Logistics Costs\u003c/li\u003e\n \u003cli\u003eRenewable Energy Adoption vs. Food CPI\u003c/li\u003e\n \u003cli\u003eLogistics Costs vs. Food CPI\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003cstrong\u003e2.2.3 Time-Series Analysis\u003c/strong\u003e\u003c/h4\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eARIMA (Autoregressive Integrated Moving Average) Model\u003c/strong\u003e: Used to forecast transport costs by examining past values and trends.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eVAR (Vector Autoregression) Model\u003c/strong\u003e: Captures the dynamic interdependencies among renewable adoption, logistics costs, and food CPI over time.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003cstrong\u003e2.2.4 Regression Analysis\u003c/strong\u003e\u003c/h4\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eMultiple Linear Regression (MLR)\u003c/strong\u003e: Quantifies the effect of independent variables on transport costs and food CPI. The regression models take the form:\u0026nbsp;\u003col\u003e\n \u003cli\u003eLogistics Cost = f(Renewable Adoption, Oil Price, Government Policy, GDP Growth, Exchange Rate)\u003c/li\u003e\n \u003cli\u003eFood CPI = f(Renewable Adoption, Oil Price, Government Policy, GDP Growth, Exchange Rate)\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/li\u003e\n \u003cli\u003eDiagnostic tests such as multicollinearity (Variance Inflation Factor), heteroscedasticity (Breusch-Pagan test), and autocorrelation (Durbin-Watson test) are conducted to ensure model validity.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003cstrong\u003e2.2.5 Granger Causality Tests\u003c/strong\u003e\u003c/h4\u003e\n\u003cul\u003e\n \u003cli\u003eDetermines whether changes in renewable energy adoption \u003cstrong\u003epredict\u003c/strong\u003e changes in logistics costs and food CPI.\u003c/li\u003e\n \u003cli\u003eHypotheses tested:\u0026nbsp;\u003cul\u003e\n \u003cli\u003eH1: Renewable Energy Adoption Granger-causes Transport Costs.\u003c/li\u003e\n \u003cli\u003eH2: Renewable Energy Adoption Granger-causes Food CPI.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis methodological framework enables a comprehensive examination of the impact of renewable energy adoption on economic variables. The integration of multiple statistical techniques ensures robustness in the analysis, providing valuable insights for policymakers and businesses aiming to transition to sustainable energy solutions.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eFindings and Analysis\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003cstrong\u003e4.1. Descriptive Statistical Analysis\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eDescriptive statistics provide a foundational understanding of key variables, summarizing data through measures such as mean, standard deviation, and distribution analysis. The analysis focuses on three main aspects: renewable energy adoption, transport costs, and food price inflation.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e4.2 Key Statistical Measures\u003c/strong\u003e\u003c/h4\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eMean Analysis:\u003c/strong\u003e\n \u003col\u003e\n \u003cli\u003eRenewable energy adoption rate averaged \u003cstrong\u003e3.64%\u003c/strong\u003e, indicating relatively low but consistent adoption.\u003c/li\u003e\n \u003cli\u003eTransport costs were found to be \u003cstrong\u003emoderate and stable\u003c/strong\u003e over time.\u003c/li\u003e\n \u003cli\u003eFood Consumer Price Index (CPI) showed a \u003cstrong\u003egradual increase\u003c/strong\u003e, influenced by transport costs and economic conditions.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eStandard Deviation Analysis:\u003c/strong\u003e\n \u003col\u003e\n \u003cli\u003eRenewable energy adoption showed \u003cstrong\u003emoderate variation (SD = 2.59%)\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e indicating occasional fluctuations.\u003c/li\u003e\n \u003cli\u003eTransport costs exhibited \u003cstrong\u003esignificant fluctuations\u003c/strong\u003e, influenced by factors like fuel prices and supply chain disruptions.\u003c/li\u003e\n \u003cli\u003eFood CPI had \u003cstrong\u003ehigher variability\u003c/strong\u003e, signifying sensitivity to economic and logistical shifts.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDistribution Analysis:\u003c/strong\u003e\n \u003col\u003e\n \u003cli\u003eRenewable adoption exhibited \u003cstrong\u003eright-skewed distribution\u003c/strong\u003e, indicating occasional policy-driven spikes.\u003c/li\u003e\n \u003cli\u003eTransport costs showed \u003cstrong\u003emoderate skewness\u003c/strong\u003e, reflecting market disruptions.\u003c/li\u003e\n \u003cli\u003eFood CPI was \u003cstrong\u003eright-skewed\u003c/strong\u003e, implying periodic price surges.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003cstrong\u003e4.3 key takeaway\u003c/strong\u003e\u003c/h4\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eThe mean values suggest \u003cstrong\u003egeneral stability\u003c/strong\u003e in the analyzed variables.\u003c/li\u003e\n \u003cli\u003eModerate standard deviations in renewable adoption and transport costs indicate \u003cstrong\u003eperiodic shifts\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003eSkewed distributions in food CPI and transport costs suggest \u003cstrong\u003eexternal shocks impact these variables\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003cstrong\u003e4.2. Correlation Analysis\u003c/strong\u003e\u003c/h3\u003e\n\u003ch4\u003e\u003cstrong\u003e4.2.1 Overview\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003ePearson correlation analysis was conducted to assess relationships between renewable energy adoption, logistics costs, and food price inflation.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003e4.2.2 Key Findings\u003c/strong\u003e\u003c/h4\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eRenewable Energy Adoption vs. Transport Costs\u003c/strong\u003e\u003cstrong\u003e: \u003cstrong\u003eWeak negative correlation (-0.1233)\u003c/strong\u003e\u0026nbsp;\u003c/strong\u003esuggests that renewable energy adoption does not significantly reduce logistics costs in the short term.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRenewable Energy Adoption vs. Food CPI\u003c/strong\u003e\u003cstrong\u003e: \u003cstrong\u003eStrong positive correlation (0.6934)\u003c/strong\u003e\u0026nbsp;\u003c/strong\u003eindicates that increasing renewable energy adoption is associated with rising food prices.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTransport Costs vs. Food CPI\u003c/strong\u003e\u003cstrong\u003e: \u003cstrong\u003eModerate negative correlation (-0.3138)\u003c/strong\u003e\u0026nbsp;\u003c/strong\u003esuggests thatrising transport costs might encourage supply chain optimizations that stabilize food prices\u003cstrong\u003e.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003cstrong\u003e4.2.3 Implications\u003c/strong\u003e\u003c/h4\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eInvestment in Green Logistics\u003c/strong\u003e: While renewable adoption does not immediately lower transport costs, sustained investment in electric/hydrogen-based transport could yield long-term benefits.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFood Price Stability\u003c/strong\u003e: Given the \u003cstrong\u003estrong positive correlation\u003c/strong\u003e between renewable adoption and food CPI, targeted policies are needed to mitigate food price inflation.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBalanced Transition Approach\u003c/strong\u003e: A \u003cstrong\u003egradual transition\u003c/strong\u003e to renewable energy in logistics and food supply chains is necessary to balance sustainability with economic stability.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003cstrong\u003e4.3. Time-Series Analysis\u003c/strong\u003e\u003c/h3\u003e\n\u003ch4\u003e\u003cstrong\u003e4.3.1 ARIMA Model for Transport Costs\u003c/strong\u003e\u003c/h4\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eARIMA (1,0,0) results\u003c/strong\u003e indicate that \u003cstrong\u003etransport costs depend significantly on their past values (AR coefficient = 0.9089, p \u0026lt; 0.001)\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003eDiagnostic tests reveal \u003cstrong\u003esome autocorrelation and non-normality\u003c/strong\u003e, suggesting additional external factors influencing transport costs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003ePolicy implication: Any external shock (e.g., fuel price hikes) will have prolonged effects, requiring long-term stabilization policies.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003cstrong\u003e4.3.2 Vector Autoregression (VAR) Model Findings\u003c/strong\u003e\u003c/h4\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eRenewable adoption significantly impacts transport costs (Coefficient = 3.78548, p = 0.007)\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e indicating initial cost increases during transition.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFood CPI shows a weaker response to renewable adoption and transport costs\u003c/strong\u003e, suggesting other macroeconomic influences.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTransport costs exhibit strong autoregressive behavior\u003c/strong\u003e, meaning past values significantly predict future costs.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003ePolicy implication: Investments in renewable energy should be accompanied by policies that offset short-term cost increases.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003cstrong\u003e4.3.3 Policy Implications\u003c/strong\u003e\u003c/h4\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eShort-term cost increases due to renewable adoption require targeted subsidies\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFood price responses to transport costs are weaker than expected\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eindicating the role of broader economic factors.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLong-term investments in green logistics are needed to stabilize costs\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003cstrong\u003e4. Regression Analysis\u003c/strong\u003e\u003c/h3\u003e\n\u003ch4\u003e\u003cstrong\u003e4.1 Multiple Linear Regression Findings\u003c/strong\u003e\u003c/h4\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eLogistics Cost Model\u003c/strong\u003e:\u003col\u003e\n \u003cli\u003eRenewable adoption positively impacts logistics costs \u003cstrong\u003e(\u003cstrong\u003eβ = 2.789, p \u0026lt; 0.01\u003c/strong\u003e),\u003c/strong\u003e suggesting short-term cost increases.\u003c/li\u003e\n \u003cli\u003eGovernment policies help mitigate logistics costs \u003cstrong\u003e(\u003cstrong\u003eβ = -1.234, p \u0026lt; 0.05\u003c/strong\u003e).\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003eGDP growth and oil prices also influence transport costs.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFood CPI Model\u003c/strong\u003e:\u003col\u003e\n \u003cli\u003eRenewable adoption positively impacts food prices \u003cstrong\u003e(\u003cstrong\u003eβ = 1.432, p \u0026lt; 0.05\u003c/strong\u003e\u003c/strong\u003e).\u003c/li\u003e\n \u003cli\u003eOil price increases lead to higher food inflation.\u003c/li\u003e\n \u003cli\u003eFavorable government policies reduce food price inflation (\u003cstrong\u003eβ = -0.934, p \u0026lt; 0.05\u003c/strong\u003e\u003cstrong\u003e).\u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003cstrong\u003e4.2 Key Insights\u003c/strong\u003e\u003c/h4\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eRenewable adoption leads to short-term cost increases in logistics and food prices\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGovernment interventions play a key role in stabilizing economic impacts\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMacroeconomic stability (exchange rates, GDP growth) significantly influences cost structures\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\n \u003ch3\u003e\u003cstrong\u003eGranger Causality Analysis\u003c/strong\u003e\u003c/h3\u003e\n \u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4\u003e\u003cstrong\u003e5.1 Key Findings\u003c/strong\u003e\u003c/h4\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eRenewable Energy Adoption → Transport Costs\u003c/strong\u003e\u003cstrong\u003e: \u003cstrong\u003eSignificant causality\u003c/strong\u003e,\u003c/strong\u003e confirming that renewable energy adoption influences transport costs over time.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRenewable Energy Adoption → Food CPI\u003c/strong\u003e\u003cstrong\u003e: \u003cstrong\u003eNo significant causality\u003c/strong\u003e\u003c/strong\u003e, indicating that other economic factors have a stronger role in food price movements.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch4\u003e\u003cstrong\u003e5.2 Policy Implications\u003c/strong\u003e\u003c/h4\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eInvestment in Green Logistics\u003c/strong\u003e: Long-term subsidies for electric and hydrogen-powered transportation can reduce initial cost burdens.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eManaging Food Price Inflation\u003c/strong\u003e: Policies should balance renewable adoption incentives with measures that stabilize food production costs.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBalancing Short-Term and Long-Term Costs\u003c/strong\u003e: The transition to renewable energy involves short-term cost increases, requiring strategic interventions.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe findings confirm that \u003cstrong\u003erenewable energy adoption influences transport costs and food prices, with short-term cost increases but potential long-term benefits\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Policy interventions, such as subsidies and infrastructure investments, are essential for mitigating economic disruptions during the transition.\u003c/p\u003e"},{"header":"4.\tDiscussions ","content":"\u003cp\u003e\u003cstrong\u003eRenewable Energy Adoption and Logistics Costs: Short-Term Pressures vs. Long-Term Benefits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings of this study confirm that renewable energy adoption in logistics is associated with initial cost increases, largely due to infrastructure investments in EVs, charging stations, and renewable-powered cold-chain systems. Similar results are reported in international and Indian contexts, where the upfront costs of green transitions temporarily raise logistics expenses (Havenga, 2010; Mohapatra et al., 2021; Jagtap et al., 2020). For instance, case studies on India\u0026rsquo;s renewable energy integration into transport highlight that solar-powered EV systems and biofuels can only achieve cost competitiveness after sustained adoption (Barton \u0026amp; Thomson, 2021; Basavaraj et al., 2012).\u003c/p\u003e\n\u003cp\u003eNevertheless, long-term benefits such as reduced fossil fuel dependency, improved energy security, and resilience against oil price shocks are widely recognized (Varghese, 2017; Sultan et al., 2020; Kaushik \u0026amp; Shastri, 2024). International experiences further suggest that renewable-powered cold storage, green packaging, and sustainable logistics automation can stabilize costs over time, provided policy support is in place (Lingaitienė \u0026amp; Burinskienė, 2024; Meneghetti et al., 2018; Tsolakis et al., 2023). Thus, governments should complement renewable adoption with targeted subsidies, tax incentives, and infrastructure development to minimize short-term disruptions (Desai, 2021; Lu et al., 2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLogistics Costs and Food Price Inflation: A Complex Relationship\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContrary to conventional theory, the correlation analysis in this study showed a moderate negative relationship between logistics costs and food price inflation. This suggests that rising transport costs may encourage efficiency gains, such as route optimization, local sourcing, and supply chain digitization (Kharaishvili \u0026amp; Gechbaia, 2023; Rathore et al., 2021). Previous studies also indicate that while logistics costs significantly influence food inflation, broader structural drivers including climate shocks, policy inefficiencies, and global market volatility often overshadow this relationship (Mishra \u0026amp; Roy, 2012; Bhattacharya, 2016; Chandrasekhar, 2012; Dash \u0026amp; Lugauer, 2024).\u003c/p\u003e\n\u003cp\u003eFood inflation in India has historically been shaped by systemic inefficiencies, including supply chain distortions (Bhattacharya, 2016), APMC market rigidities (Neha Tomar, 2013), and cold-chain bottlenecks (Maheshwar \u0026amp; Chanakwa, 2006). Disruptions during the COVID-19 pandemic further underscored the fragility of Indian food supply chains, amplifying the impact of logistics inefficiencies on food inflation (Reardon et al., 2020; Sudan \u0026amp; Taggar, 2021). Comparative evidence also shows that international food and fuel price shocks are quickly transmitted to domestic inflation, exacerbating volatility in India (Sultan et al., 2020; Zhang et al., 2024).\u003c/p\u003e\n\u003cp\u003eThese findings indicate that while logistics costs remain a key determinant of food prices, effective policy interventions must address both micro-level inefficiencies (cold storage, intermediaries, transport resilience) and macro-level shocks (oil prices, global food markets, climate risks).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRenewable Energy Adoption and Food Price Inflation: Inflationary Risks During Transition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study confirms a strong positive correlation between renewable energy adoption and food price inflation, reinforcing concerns that green logistics integration may exert inflationary pressures in the short run. This aligns with earlier evidence that renewable-powered cold storage, biofuel logistics, and EV-based food transport require significant capital investment, often passed on to consumers (Mogale et al., 2020; Gawusu, 2024; Gorjian et al., 2022). Similarly, empirical analyses show that international oil price volatility, monetary policies, and renewable adoption costs together shape food price movements in emerging economies (Anand et al., 2014; Mishra \u0026amp; Agarwal, 2021; Dar \u0026amp; Asif, 2023).\u003c/p\u003e\n\u003cp\u003eHowever, long-term benefits remain compelling. Renewable adoption can shield economies from fossil fuel volatility, promote food supply chain resilience, and stabilize transport costs once infrastructure matures (Bekun, 2022; Abid \u0026amp; Saqlain, 2023; Umar \u0026amp; Wilson, 2024). Moreover, research indicates that green packaging innovations and circular economy practices can reduce the inflationary burden associated with renewable adoption (Panghal et al., 2024; Lingaitienė \u0026amp; Burinskienė, 2024). Thus, the inflationary risks of renewable logistics transitions should be viewed as temporary, requiring careful policy sequencing rather than as deterrents to sustainability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolicy Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShort-Term Subsidies and Incentives;\u0026nbsp;\u003c/strong\u003eTo counter the immediate cost burden of renewable adoption, governments should provide targeted subsidies, concessional loans, and tax rebates to logistics firms investing in EVs, cold-chain renewables, and green warehousing (Lu et al., 2020; Desai, 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengthening Food Supply Chains;\u0026nbsp;\u003c/strong\u003eAddressing inflation requires interventions beyond logistics costs such as investments in cold storage, market reforms, and digital logistics systems (Maheshwar \u0026amp; Chanakwa, 2006; Jagtap et al., 2020). Strengthening postharvest infrastructure can reduce losses and moderate price pressures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMitigating Global Shock Transmission;\u0026nbsp;\u003c/strong\u003eSince global oil and food commodity markets strongly influence Indian inflation, macroeconomic buffers such as foreign exchange stabilization and trade diversification policies are critical (Varghese, 2017; Sultan et al., 2020; Zhang et al., 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEncouraging Circular Economy and Green Packaging;\u0026nbsp;\u003c/strong\u003eCircular economy innovations including renewable packaging, IoT-based logistics, and energy-efficient food storage can offset the inflationary effects of renewable adoption (Lingaitienė \u0026amp; Burinskienė, 2024; Singh \u0026amp; Roy, 2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGradual and Phased Transition;\u0026nbsp;\u003c/strong\u003eA sudden shift to renewables risks escalating logistics costs and food prices. A gradual, phased transition, supported by policies on trade, energy, and food inflation, is necessary to balance sustainability and economic stability (Kiesecker et al., 2019; Sudan \u0026amp; Taggar, 2021).\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study examined the relationship between renewable energy adoption, logistics costs, and food price inflation using a combination of descriptive statistics, correlation analysis, time-series modelling, regression analysis, and Granger causality tests. The findings provide valuable insights into the economic implications of transitioning to renewable energy in logistics and its potential impact on food prices. This conclusion summarizes the key takeaways and their policy implications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Key Findings\u003c/strong\u003e\u003c/p\u003e\n\u003col class=\"decimal_type\"\u003e\n \u003cli\u003e\u003cstrong\u003eRenewable Energy Adoption Increases Logistics Costs in the Short Term:\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eRegression analysis showed that a 1% increase in renewable energy adoption led to a 2.79% increase in logistics costs.\u003c/li\u003e\n \u003cli\u003eThe time-series ARIMA model confirmed the persistence of logistics cost fluctuations over time.\u003c/li\u003e\n \u003cli\u003eThe increase in costs is primarily due to the high initial investment in green transport infrastructure (e.g., electric vehicles, charging stations).\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eThe Relationship Between Logistics Costs and Food Price Inflation is Complex:\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eA moderate negative correlation (-0.3138) was observed between logistics costs and food price inflation, suggesting that rising transport costs might encourage supply chain efficiencies that stabilize food prices.\u003c/li\u003e\n \u003cli\u003eRegression results showed that while logistics costs influence food prices, their effect is overshadowed by macroeconomic factors such as oil price volatility and government interventions.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRenewable Energy Adoption is Positively Correlated with Food Price Inflation:\u003c/strong\u003e\n \u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eA strong positive correlation (0.6934) was found between renewable adoption and food CPI, indicating that as renewable energy adoption increases, food prices also rise.\u003c/li\u003e\n \u003cli\u003eRegression analysis confirmed that a 1% increase in renewable adoption corresponds to a 1.43% increase in food price inflation.\u003c/li\u003e\n \u003cli\u003eThe cost implications of transitioning to renewable-powered food logistics and processing appear to drive this inflationary effect.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRenewable Energy Adoption Granger-Causes Logistics Costs but Not Food Prices:\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eGranger causality tests confirmed that changes in renewable energy adoption significantly influence logistics costs over time.\u003c/li\u003e\n \u003cli\u003eHowever, no significant causality was found between renewable energy adoption and food CPI, suggesting that external macroeconomic factors play a larger role in food price fluctuations.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003cstrong\u003e2. Policy Implications\u003c/strong\u003e\u003c/h3\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eFinancial Support for Green Logistics:\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eShort-term cost increases due to renewable energy adoption can be mitigated through subsidies, tax incentives, and low-interest financing for green logistics investments.\u003c/li\u003e\n \u003cli\u003eGovernment policies should support logistics firms in adopting electric or hydrogen-powered fleets to ensure long-term cost reductions.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTargeted Measures to Control Food Price Inflation:\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eSince renewable energy adoption is linked to rising food prices, policymakers should implement food price stabilization measures, such as agricultural subsidies and supply chain optimization programs.\u003c/li\u003e\n \u003cli\u003eEncouraging localized food production and renewable-powered cold storage facilities can reduce cost pressures on food supply chains.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eInvestment in Renewable Infrastructure to Reduce Costs:\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eDeveloping large-scale renewable energy infrastructure, such as nationwide charging networks and biofuel production facilities, can help lower logistics costs over time.\u003c/li\u003e\n \u003cli\u003eCoordinated investment between the government and private sector can accelerate cost reductions in green logistics.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGradual Transition to Minimize Economic Disruptions:\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eA phased approach to integrating renewable energy into logistics and food supply chains can prevent sharp cost increases.\u003c/li\u003e\n \u003cli\u003ePolicymakers should adopt transition frameworks that balance sustainability goals with economic stability.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFuture Research on External Economic Factors:\u003c/strong\u003e\n \u003cul\u003e\n \u003cli\u003eThe weak causality between transport costs and food inflation suggests that other macroeconomic factors (e.g., oil prices, currency fluctuations, and trade policies) influence food prices.\u003c/li\u003e\n \u003cli\u003eFurther studies should integrate additional variables to refine the understanding of the renewable energy-food price nexus.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003e\u003cstrong\u003e3. Contribution to Research and Future Directions\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThis study contributes to the growing body of literature on sustainable logistics and food supply chains by providing empirical evidence on the economic trade-offs of renewable energy adoption. The findings highlight the need for a balanced approach to sustainability, where environmental benefits are achieved without causing significant economic disruptions.\u003c/p\u003e\n\u003cp\u003eFuture research should:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eInvestigate long-term cost reductions from renewable energy adoption in logistics.\u003c/li\u003e\n \u003cli\u003eExplore the role of government policies in mitigating initial cost increases.\u003c/li\u003e\n \u003cli\u003eAssess regional differences in renewable adoption and its economic impact on food prices.\u003c/li\u003e\n \u003cli\u003eApply machine learning and econometric models to predict future trends in green logistics costs and food inflation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe transition to renewable energy in logistics presents both challenges and opportunities. While short-term cost pressures exist, strategic policy interventions can ensure that long-term economic and environmental benefits outweigh initial disruptions. By fostering sustainable logistics, governments and businesses can achieve a greener, more resilient supply chain while safeguarding economic stability.\u003c/p\u003e\n\u003cp\u003eThis research underscores the importance of integrating economic, policy, and technological considerations in renewable energy transitions. A well-planned shift toward sustainable logistics can pave the way for a future where environmental responsibility and economic efficiency coexist.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e1. Data Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Ethics and Guidelines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study followed all relevant ethical guidelines for research and publication. The research did not involve human participants or animals, and therefore did not require formal ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. The study did not involve human participants requiring informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Consent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. The manuscript does not contain data from any individual person, and therefore consent for publication was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Ethics, Consent to Participate, and Consent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not involve human participants, patient records, or clinical samples. The research is based on economic modeling, secondary data analysis from publicly available sources, and conceptual frameworks related to renewable energy adoption in logistics. Therefore:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eEthics approval and consent to participate: Not applicable, as no human subjects or clinical interventions were involved in the study.\u003c/li\u003e\n \u003cli\u003eConsent for publication: Not applicable, as the manuscript does not contain any identifiable personal data, images, or case details of individuals.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is not a clinical trial and does not involve any form of medical, clinical, or patient-related interventions. The research exclusively focuses on analyzing the economic implications of renewable energy adoption in logistics, particularly its effect on transportation costs and food price inflation, using secondary data sources and conceptual modeling. Hence, trial registration details (registry name, trial number, and date) are not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJanardhana Anjanappa: Conceptualized the research idea, designed the study framework, supervised the research process, and contributed to the interpretation of results and policy implications. Drafted and critically revised the manuscript for intellectual content.Shridhar M. Samant: Collected and curated secondary data, performed statistical and econometric analyses (descriptive statistics, correlation, ARIMA, VAR, regression, Granger causality), and contributed to the preparation of results and discussion sections.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSharma RK, Dharni K, Smagh A, Vashisht P. Relationship between logistics cost and relative firm efficiency in Indian food processing sector. J Econ Manage Trade. 2021;27(1):42\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar S, Bharj RS. Solar hybrid e-cargo rickshaw for urban transportation demand in India. Transp Res Procedia. 2020;48:1998\u0026ndash;2005.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoy S, Mohanty RP. Green logistics operations and its impact on supply chain sustainability: An empirical study. 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Int Journal\u0026rsquo;s Res J Sci IT Manage. 2014;3(10):30\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Renewable Energy, Logistics Costs, Food Price Inflation, Sustainable Transportation, Green Supply Chains","lastPublishedDoi":"10.21203/rs.3.rs-7549450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7549450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe transition to renewable energy in logistics is increasingly recognized as a crucial step toward sustainability. However, its economic implications, particularly on transportation costs and food price inflation, remain underexplored. This study examines the relationship between renewable energy adoption, logistics costs, and food price inflation using descriptive statistics, correlation analysis, time-series modelling (ARIMA, VAR), multiple linear regression, and Granger causality tests. The findings indicate that renewable energy adoption leads to short-term increases in logistics costs due to infrastructure investments, though long-term benefits may materialize. A strong positive correlation was found between renewable energy adoption and food price inflation, suggesting that green logistics integration could drive initial cost surges. The Granger causality test confirmed that renewable adoption influences logistics costs but does not directly cause food price inflation, implying the role of additional macroeconomic factors. Policy interventions, such as targeted subsidies, infrastructure investments, and gradual transition strategies, are recommended to balance sustainability with economic stability. This research contributes to the growing body of literature on sustainable logistics and offers insights for policymakers and industry stakeholders.\u003c/p\u003e","manuscriptTitle":"Economic Implications of Renewable Energy Adoption in Logistics and Its Impact on Transportation Costs and Food Price Inflation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 11:39:45","doi":"10.21203/rs.3.rs-7549450/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e4327b5c-8c8f-4e6e-9f1a-ceee467c89ca","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T08:57:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 11:39:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7549450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7549450","identity":"rs-7549450","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}