Introduction

The electricity demand is continuously increasing with population growth and every nation worldwide strives to increase its generation capacity. Coal and other fossil fuels have always been used to generate electricity in thermal power plants. But over time, some issues cropped up with their excessive use. The combustion of coal and other fossil fuels causes a lot of anthropogenic emissions to the environment. These emissions have adverse effects on the environment along with human health. In these emissions, some gases, specifically Sulphur-Dioxide (SO2), Nitrous Oxide (NOx), and Ammonia (NH3) mix with water vapor in the atmosphere and also, along with rain water, contribute to the acidification of water in the nearby areas. Annually 70 Tg (1012 g) of sulphur emission comes from fossil fuel combustion as compared to 8 Tg from volcanoes and 2–8 Tg from wildfire1. Thus, electricity generation from conventional sources generates a lot of pollutants that result in environmental degradation through acidification. These acidic gases that are somehow released into the air are mixed with water vapour formed into an acid. This precipitated water vapours (acid), which soil, plants, and surface waters absorb, leads to soil acidity, damage to leaves, etc. Moreover, it also leads to an increase in heavy metals take up as compared to nutrients take up, adversely affecting plants’ growth2. The emissions of sulphur dioxide (SO2) are converted to sulphuric acid (H2SO4) when combined with water vapour in the atmosphere. Thus, areas downwind of the power plant receive heavy acid rain.

One of the most suitable alternatives to mitigate the acidification issue is the generation of electricity using renewable energy sources. Renewable energy-based electricity generation is more expedient in terms of its renewing nature, availability, and less load on the environment. Promoting electricity generation through renewable sources is also helpful for decentralizing applications and, thus, a viable source for remote areas. Although the power generated using renewable energy sources is considered to have no environmental impacts, this is not completely true. Renewable energy-based power plants contribute to emissions during the construction phase and indirect emissions in the operation phase of their life cycle3.

Among all renewable energy sources, hydropower is considered to be a promising electricity generation source. All hydropower plants use the energy of water to generate electricity, and it is carried out at both large and small scales. Large hydropower projects have the involvement of enormous reservoirs, rehabilitation of communities, high capital investment, and environmental issues. Small hydropower (SHP) plants are the appropriate solution to the impediments associated with large hydropower projects. They are also receiving special attention due to the rising environmental and political issues and high prices associated with fossil fuel-based power production systems4,5. No international consensus exists to discriminate between large and small hydropower projects. The power output generally characterizes SHP plants. Around the world, countries have fixed the maximum limit of a plant capacity to be considered as SHP, which varies from 5 to 50 MW. Initially, in India, the maximum size of a plant that comes under SHP was 15,000 kW6, and later this upper limit was increased to 25,000 kW. Further, SHP projects can be elaborated as hydro projects that cost equal to or less than Rs—one hundred crores (1000 million) nearly to a plant capacity of around 25,000 kW. India is a significant context for this study due to its growing energy demand and large untapped potential for small hydropower (SHP). SHP is critical for remote regions, and India aims to reduce its reliance on fossil fuels. The country faces several environmental challenges and reducing greenhouse gases (GHGs) under international agreements. This study helps optimize SHP development, minimizing environmental impacts and supporting India’s sustainability goals. The estimated SHP potential in India is around 21,135.37 MW (7135 identified sites), of which around 4379 MW capacity plants are operational, and many more are under construction7, highlighting a vast opportunity for SHP development. The diverse geography and water resources of India provide an ideal landscape for SHP projects, especially in remote and hilly regions where large-scale energy infrastructure is challenging. Moreover, India’s commitment to reducing carbon emissions under international agreements further necessitates exploring sustainable and low-emission energy solutions, like SHP. These factors make India a critical case study for assessing the environmental impacts, particularly the acidification potential, of SHP projects. Understanding these impacts is essential for guiding future policy and ensuring the sustainable development of this resource in India. In 1897, the first hydropower project was established at Sidrapong near Darjeeling in India capacity of 130 kW. At the same time, other hydropower projects in the year 1902, 1907, and 1930, Shivasundaram in Mysore, Galogi in Mussoorie, and Jubbal in Shimla, respectively, were commissioned and are still in operation8. The Sidrapong power plant has undergone renovation and modernization and has an installed capacity of 600 kW. SHP projects are considered an impressive solution to obtain cleaner power however, their environmental impacts must be addressed thoroughly. The growth of SHP projects leads to environmental issues that need extreme attention9. The release of gases such as SO2, NOx, Ammonia (NH3), etc., is associated with the life cycle of an SHP project. Acidification potential, typically assessed through Life Cycle Assessments (LCA), refers to the capacity of certain emissions to contribute to acid rain, which can have detrimental effects on ecosystems. In recent years, LCA has emerged as a critical tool for assessing the environmental impacts of various energy systems and technologies. Zahedi et al.10 used LCA to evaluate transparent solar cells, comparing their environmental and health impacts with traditional solar technologies. Their study highlighted the crucial role LCA plays in advancing sustainable energy solutions and underscored the need for environmental impact assessments in developing new technologies.

Building on these insights, Zahedi et al.11 and Khalili et al.12 broadened the use of LCA to assess more energy-intensive systems, such as cryptocurrency mining and 3D-printable buildings. Their work demonstrated the significant environmental impacts of modern industrial processes, reinforcing the importance of LCA in fostering sustainable technological developments. Aslani et al.13 emphasized the growing need for modern LCA methods to evaluate environmental footprints across a range of industries, from emerging technologies to traditional sectors. Their research called for innovative strategies to reduce environmental impacts in diverse fields, from digital payment systems to conventional construction.

In line with these studies, the current research applies LCA to estimate the acidification potential of small hydropower (SHP) projects in India. By carefully quantifying sulphur dioxide equivalents (SO2eq) released during the construction, operation, and maintenance phases, this study explores how factors such as project capacity and type affect overall emissions. The findings provide essential insights for policymakers and developers seeking to minimize the environmental impacts of SHP projects in India.

Some studies have been reported on estimating the acidification potential of various power generation systems. Keller et al.14 have assessed the life cycle environmental effect of the alternative and conventional feedstocks for olefin production using GABI software. Results show that, for a possible solution, the integration of renewable energy is required. Valente et al.15 studied the life cycle acidification for hydrogen energy systems and found no relation between gross carbon emissions and acidification. In the present study, the estimation of SO2eq gas emissions from various SHP projects in India is carried out through LCA. In the view of above discussion, an attempt has been made to calculate the total life cycle SO2eq emissions of three different SHP systems (dam-toe, canal, and run-of-river). Correlations are also established for different schemes and sizes based on the expected amount of SO2eq emissions.

Novelty and significance of this research

The key contributions and significance of this study are as follows:

  • Focus on Small Hydropower (SHP): This research explores the environmental impact of SHP projects, with a particular emphasis on acidification potential—an area that has received limited attention in India’s renewable energy landscape.

  • Application of LCA Methodology: The study distinctively employs Life Cycle Assessment (LCA) to measure SO₂ emissions from SHP projects in India is a novel work that needs to be addressed.

  • Development of Predictive Models: It establishes correlations using regression analysis to forecast SO₂ emissions across various SHP project types, considering factors such as capacity and head.

  • Comprehensive Data Analysis: Drawing on data from 123 SHP projects across India, the research provides robust and generalizable results that can inform future efforts to mitigate environmental impacts.

  • Practical and Policy Relevance: The derived correlations serve as valuable tools for policymakers and developers to effectively reduce the acidification potential of SHP projects.

  • India-Specific Environmental Framework: By creating a framework tailored to Indian SHP projects, this study addresses a critical gap in the assessment of environmental impacts in the country.

The study’s significance lies in its integration of environmental science and renewable energy policy, providing critical insights for promoting sustainable SHP development.

Methodology

This section outlines the methodology used to estimate the acidification potential of Small Hydropower (SHP) projects in India. The study applies the Life Cycle Assessment (LCA) methodology, specifically focusing on the calculation of sulfur dioxide equivalents (SO2eq) to assess acidification potential. The LCA methodology followed a cradle-to-gate approach, covering the construction, operation, and maintenance phases of SHP projects. The demolition phase was excluded due to its insignificance in the life cycle of SHP projects.

Goal and scope

The primary goal of this Life Cycle Assessment (LCA) is to assess the acidification potential linked to small hydropower (SHP) projects in India. By quantifying SO₂-equivalent (SO2eq) emissions across the entire life cycle of different SHP configurations—such as run-of-river, canal-based, and dam-toe systems. The study aims to provide a thorough evaluation of their environmental impacts. The overarching aim is to create predictive models that correlate SO₂ emissions with critical project factors like installed capacity and head height. These models are intended to act as practical tools, aiding in the design and execution of future SHP projects, helping to minimize acidification potential, and promoting sustainable hydropower development.

Scope

  • System Boundaries: The LCA follows a cradle-to-gate approach, covering emissions during construction, operation, and maintenance, while excluding the demolition phase, which is generally insignificant for SHP projects. The analysis covers:

    • Civil works (e.g., dams, canals, penstocks)

    • Electro-mechanical equipment (e.g., turbines, generators, transformers)

    • Operational emissions over a 30-year lifespan

  • Functional Unit: Environmental impacts are calculated per 1 kWh of net electricity generated, allowing for comparisons across different SHP configurations.

  • Impact Category: The study focuses on acidification potential, quantified in terms of kg SO2eq per kWh, based on life cycle emissions.

  • Geographical and Temporal Scope: The research uses data from 123 SHP projects across India, standardized to the base year 2010 for economic inputs as most of them were installed before 2010.

  • Data Sources: Information is obtained from Detailed Project Reports (DPRs), site inspections, and economic input–output (EIO) tables, leveraging the Carnegie Mellon University Green Design Institute model for emissions estimation.

Life cycle assessment

Life cycle studies can be classified as process chain, input–output, and hybrid LCA. The process chain approach is used to estimate various toxic releases and energy usage from manufacturing construction materials and electro-mechanical equipment used in these schemes. The input–output-based LCA uses economic data of different countries to assess the environmental impacts. Hybrid LCA integrates input–output and process chain analysis. In the present study, economic input–output (EIO) based LCA has been carried out to evaluate life cycle GHG emissions for various SHP schemes16,17,18. The EIO-LCA approach was chosen because it allows for a broad assessment of environmental impacts by utilizing readily available economic data across sectors. This method is particularly suitable for national-level studies to cover a diverse range of small hydropower projects. Process-based LCA would require detailed, site-specific data, which is often unavailable for large-scale analyses. The hybrid LCA, though comprehensive, combines both methods but demands more extensive data, which wasn’t feasible for this study. The limitations of EIO-LCA, such as its reliance on aggregated data and its assumption of linear relationships between economic output and environmental impacts, might reduce precision compared to process-based or hybrid approaches. In this approach, the real economic data of a country/region is segregated into different sectors, which are represented in the form of EIO tables. This table is used to estimate the interdependence of each sector on one another. In evaluating the indicators such as carbon footprint, acidification potential, land use etc., a coefficient is required that is used with a matrix of sector-level environmental coefficients. The model developed by GDI at Carnegie Mellon University is used to evaluate acidification potential19. The calculation of the acidification in terms of kg-SO2eq corresponding to mj kg of a significant component (j) is analogous to the computation of the pollutant potential. Table 1 shows the general acidification potential of selected gases20,21.

$$Acidification\,(kg - SO_{2eq} ) = AP_{j} \left( {\frac{{kg - SO_{2} }}{kg}} \right) \times m_{j} (kg)$$
(1)
Table 1 Acidification potential of different gases.
Full size table

To estimate the acidification potential, the price of significant components is initially required, and a list of materials is prepared. In this study, the complete list of materials was first investigated (PCA) and then the cost of each member was estimated and its emissions were evaluated (EIO). Based on the price of the materials, their associated emissions are quantified. Here 123 SHP schemes are studied, and these schemes are commissioned in different years. The cost estimates of SHP schemes pertain to several years. To make this analysis on a common year, all the prices are inflated against the price for 2010 in the Indian rupee. To estimate its associated impact, the material/components’ price is required in US dollars. Thus, the prices are converted into their equivalent dollars by using Purchasing Power Parity (PPP) through the given formula22,23.

$$EquivalentCostinUS2002(\$)=\frac{\text{Price in INR}}{\text{PPP in 2004}}\times \frac{Index({\text{inflation}}){\text{foryear2002}}}{Index({\text{inflation}}){\text{foryear2004}}}$$

To perform an LCA study, a functional unit is to be assigned which relates environmental impact to a product. The present study takes 1 kWh of net electricity produced as a functional unit. The choice of 1 kWh of net electricity produced as the functional unit in this study is appropriate for several reasons:

  • Standardized Comparison: 1 kWh of net electricity enables direct comparison across different SHP projects, regardless of variations in size, capacity, or geographical location.

  • Alignment with LCA Standards: It aligns with LCA practices for energy projects, ensuring consistency and allowing benchmarking against similar renewable energy studies.

  • Relevance to Acidification Potential: The choice directly links acidification potential to the actual electricity output, showing the environmental impact per unit of energy produced.

  • Scalability: It allows easy extrapolation of environmental impacts for larger systems or multiple projects, supporting broader assessments at regional or national levels.

Acidification potential is estimated regarding SO2eq emissions per unit of net electricity generation based on general acidification equivalents expressed relative to SO220,21.

$$SO_{2} \,emissions\,\left( {\frac{{g.SO_{2eq.} }}{{kWh_{e} }}} \right) = \frac{{Total\,SO_{2} \,emissions\,\,throughout\,\,its\,\,life - cycle\,\,(g - SO_{2} )}}{{Annual\,Power\,Generation\,\,\left( {\frac{{kWh_{e} }}{year}} \right) \times life\,time\,\,\left( {year} \right)}}$$

To perform LCA, an SHP scheme is divided into four stages: (i) electro-mechanical (E&M) equipment (ii) civil works (iii) operation & maintenance and; (iv) decommissioning. In the present study, demolition of the plant is not considered in the life cycle as it is pretty uncommon to demolish SHP plants. Three types of SHP projects are analyzed, viz. run-of-river, canal-based, and dam-toe SHP. Further economic data for around 123 SHP projects (including run-of-river, canal-based, and dam-toe type SHP projects) have been obtained. The data have been collected by visiting some project sites and detailed project reports (DPRs). The lifetime of SHP projects is taken as 30 years, and the SHP projects’ details are provided in Table 2.

Table 2 Acidification data of SHP projects.
Full size table

Data sources

The data for this study were sourced from Detailed Project Reports (DPRs), site visits, and economic input–output (EIO) tables. While DPRs are reliable industry-standard documents, there is the potential for variations due to differences in site-specific conditions, geographic factors, and economic assumptions. To mitigate such biases, standardized the economic data to the base year 2010 and conducted sensitivity analyses to assess the impact of variations in material costs and site conditions on the overall results. Site visits also provided an additional layer of validation. However, minor regional differences and other external factors may introduce slight variability, which we acknowledge as a limitation of the study. This discussion clarifies the reliability of the data used for our life cycle acidification potential estimations.

Life cycle interpretation

In Life Cycle Assessment (LCA), Life Cycle Interpretation is pivotal for understanding the environmental impacts of the small hydropower (SHP) projects analyzed in this study. During this phase, the results from the Life Cycle Inventory (LCI) and Life Cycle Impact Assessment (LCIA) are evaluated to derive meaningful conclusions regarding the acidification potential of SHP projects.

In this study, the interpretation phase involved a comprehensive analysis of the sulfur dioxide equivalent (SO2eq) emissions data obtained during the construction, operation, and maintenance phases of SHP projects. The analysis was conducted across three types of SHP configurations: run-of-river, canal-based, and dam-toe systems. Key parameters such as project capacity, head height, and scheme type were considered to develop correlations that predict SO2eq emissions.

Limitations and assumptions

The EIO-based LCA method follows a linear model, and it signifies that the variation of $1000 in demand of any economic activity in results will give ten times the consequences of $100 variation in the demand. The outcomes will further show the impacts of the sector over its output/production along with augmented demand. In most sections, no direct relationship of results is associated with the operation and end-of-life phase. The EIO models present a single nation’s economies; imports and exports are the primary elements of their transactions. Similar production attributes in imports are assumed owing to the comparable products manufactured in the nation’s interest19.

Small hydropower

In India, hydropower projects with a capacity less than or equal to 25 MW are considered SHP projects. The SHP projects can be further classified as Run-of-River type, Canal based, and; Dam toe schemes.

Run-of river SHP schemes

The process of power generation in a run-of-river scheme is simple. The hydraulic energy extracted from the water stream drives a turbine and generator. The volume and net difference in the height of the source and water outlet are the main factors that determine the energy extracted from water. The run-of-river SHP schemes are established for SHP generation and don’t involve the construction of big reservoirs as required in other large hydropower plants. Run-of-river projects utilize persistent and continuous natural water flow and are thus established on a river. In fact, as per the definition, a run-of-river plant should have water storage that can provide a continuous water supply of 48 h. The stream is diverted from the river through a weir and then Forebay (a small head tank) towards the penstock, delivering water downhill to the powerhouse. Thus, the energy of water generated through gravity is utilized to run the turbines in the powerhouse, which convert this energy into electricity using a generator. The water is further redirected back to the flow of the river.

Correlation development for run-of river SHP schemes

The data obtained from EIO-LCA method is used for the regression analysis and development of statistical correlations. A curve of SO2 emissions in g-SO2eq/kWhe versus installation capacity (C) kW, is shown in Fig. 1. SO2eq emissions tend to decrease with increasing capacity for run-of-river SHP schemes. This relationship demonstrates that larger capacity installations benefit from economies of scale, leading to lower emissions per unit of electricity generation. This is significant for optimizing project size to minimize environmental impact. The data considered in the curve is for the projects having head (H) ranging from 10 to 90 m. The relation for SO2 emission in terms of capacity is expressed as:

$$SO_{2} emission = A_{0} \times C^{ - 0.39}$$
(4)
Fig. 1
Fig. 1
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Variation of SO2eq emissions with capacity for head range 10–90 m for run-of river schemes.

The coefficient A0 will depend on other parameters. The other parameter considered in the study is the head. Figure 2 shows curve between the values of SO2 emissions/Capacity−0.39 against the head. Figure 2 indicates that emissions per unit of capacity are influenced by head height in run-of-river projects. With higher heads, additional civil infrastructure is required, which contributes to higher emissions. This highlights the trade-off between energy extraction efficiency and environmental impact in high-head schemes. The final correlation for estimation of SO2 emissions can be expressed as:

$$SO_{2} emission/C^{ - 0.39} = 2.286 \times H^{0.046}$$
(5)
$$SO_{2} emission = 2.286 \times H^{0.046} \times C^{ - 0.39}$$
(6)
Fig. 2
Fig. 2
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Variation of SO2eq emissions/capacity−0.39 with head for run-of river SHP schemes.

Similarly, the expression of SO2 emissions for the projects having head varying from 90 to 430 m can be obtained. The curve of SO2 emissions in g-SO2eq/kWhe versus capacity in kW is shown in Fig. 3. As shown in Fig. 3 higher head ranges in run-of-river schemes (90–430 m) lead to a consistent decrease in emissions with increasing capacity. However, the infrastructure required for high-head schemes moderates this reduction slightly, underscoring the need for optimized designs to balance emissions and efficiency and the obtained power law relation can be expressed as:

$$SO_{2} emission = B_{0} \times C^{ - 0.36}$$
(7)
Fig. 3
Fig. 3
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Variation of SO2eq emissions with capacity for head range 90–430 m for run-of river schemes.

The coefficient B0 will depend on other parameters i.e. head of the system. Figure 4 shows the curve plotted between the values of SO2 emissions/Capacity−0.36 and head. Figure 4 illustrates how higher heads in run-of-river projects allow for more efficient energy extraction, reducing emissions per unit of capacity. This correlation emphasizes the need to carefully select head height during the design phase to minimize environmental impact. The correlation can be expressed as:

$$SO_{2} emission/C^{ - 0.36} = 1.628 \times H^{0.03}$$
(8)
$$SO_{2} emission = 1.628 \times H^{0.03} \times C^{ - 0.36}$$
(9)
Fig. 4
Fig. 4
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Variation of SO2eq emissions/capacity−0.36 with head for run-of river schemes.

Canal based SHP schemes

The civil works involved in canal-based SHP schemes greatly depend on site characteristics. The design considerations of SHP projects differ from large hydro projects owing to financial constraints. The civil works associated with canal-based schemes include the construction of a diversion channel, spillway, and powerhouse. In low-head hydropower schemes, equipment size is relatively more extensive, and associated costs are considerably high due to handling large amounts of water. Turbines, along with governing systems, generators, switch gears, control & protection systems, auxiliary systems, transformers etc. are the main electro-mechanical equipment.

Correlation development for canal-based SHP schemes

The development of statistical correlations of the data obtained through EIO-LCA method is carried out by regression analysis. The variation of SO2 emissions in g-SO2eq/kWhe with capacity (kW) is shown in Fig. 5. This reveals that in low-head canal-based schemes, smaller capacity installations result in higher emissions per unit of electricity generated. This finding suggests that optimizing capacity is crucial for reducing emissions in low-head environments, where energy extraction is less efficient. The data used here is for the SHP projects having head varying from 1 to 7 m and the power law relation for SO2 emissions in terms of capacity can be expressed as:

$$SO_{2} emissions = A_{0} \times C^{ - 0.17}$$
(10)
Fig. 5
Fig. 5
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Variation of SO2eq emissions with capacity for head range 1–7 m for canal-based SHP schemes.

The coefficient A0 will depend on any other parameters and the parameter considered here is head. Figure 6 shows the curve showing the variation of SO2 emissions/capacity−0.17 with head. In Fig. 6, higher heads in canal-based schemes correlate with lower emissions per unit capacity, emphasizing the benefit of using higher head designs to enhance efficiency and reduce environmental impact and gives the following expression:

$$SO_{2} emissions/C^{ - 0.17} = 0.495 \times H^{ - 0.17}$$
(11)
Fig. 6
Fig. 6
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Variation of SO2eq emissions/capacity−0.17 with head for canal-based SHP schemes.

Thus, the correlation for estimation of SO2 emissions associated with canal-based projects with head varying from 1 to 7 m is obtained as;

$$SO_{2} emissions = 0.495 \times H^{ - 0.17} \times C^{ - 0.17}$$
(12)

Similarly, the correlation for estimation of SO2 emissions associated with SHP projects with head varying from 7 to 25 m can be obtained. The variation of SO2 emissions in g-SO2eq/kWhe with capacity in kW is shown in Fig. 7. As demonstrated in Fig. 7, emissions decrease as capacity increases in canal-based schemes with higher heads (7–25 m), reinforcing the importance of optimizing project capacity to reduce environmental impact. and the power law relation between them can be expressed as:

$$SO_{2} emissions = B_{0} \times C^{ - 0.14}$$
(13)
Fig. 7
Fig. 7
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Variation of SO2eq emissions with capacity for head range 7–25 m for canal-based SHP schemes.

The coefficient B0 will depend on some other parameter and can be considered as head. Figure 8 shows the variation of SO2 emissions/capacity−0.14 with head. This indicates that canal-based schemes benefit from higher heads in terms of lower emissions per unit capacity, due to improved hydraulic energy extraction at greater heads. and gives the following relation:

$$SO_{2} emissions/C^{ - 0.14} = 0.231 \times H^{0.061}$$
(14)
Fig. 8
Fig. 8
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Variation of SO2eq emissions/capacity−0.14 with head for canal-based SHP schemes.

Thus, the correlation for estimation of SO2 emissions associated with canal-based projects with head varying from 7 to 25 m is obtained as;

$$SO_{2} emissions = 0.231 \times H^{0.061} \times C^{ - 0.14}$$
(15)

Dam–toe SHP schemes

In dam-toe schemes, water is accumulated in a dam constructed across the river for other purposes like water supply, irrigation, etc., and power is produced using a controlled flow of water from the dam to the powerhouse, which is constructed at the toe of the dam. Water is carried out to the turbines using penstock directly from the dam. The dam-toe schemes are pretty popular in the southern part of India. Kaplan, propeller, tubular, and bulb turbines are quite appropriate for low-head projects and are commonly used in dam-toe schemes.

Correlation development for dam–toe SHP schemes

In dam-toe schemes also, regression analysis is used to develop correlations for predicting life cycle SO2 emissions from dam-toe schemes based on EIO-LCA method. The variation of SO2 emissions in g-SO2eq/kWhe with capacity in kW is presented in Fig. 9. This illustrates that dam-toe schemes with higher capacities tend to exhibit lower emissions per unit of electricity generation. This suggests that increasing the capacity of dam-toe projects can significantly reduce emissions, contributing to more sustainable energy production. The data used here is associated with the dam-toe schemes having head varying from 10 to 40 m and the following power law relationship is obtained:

$$SO_{2} emissions = A_{0} \times C^{ - 0.19}$$
(16)
Fig. 9
Fig. 9
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Variation of SO2eq emissions with capacity for head range 10–40 m for dam-toe SHP schemes.

The coefficient A0 will depend on any other parameter i.e., head. Further, Fig. 10 presents the variation of SO2 emissions/capacity−0.19 with head. As seen in Fig. 10, increasing the head height in dam-toe schemes leads to reduced emissions per unit capacity, due to the greater energy extraction efficiency at higher heads. This supports the use of higher-head designs for minimizing environmental impacts in dam-toe projects and gives the following relationship.

$$SO_{2} emissions/C^{ - 0.19} = 0.554 \times H^{ - 0.09}$$
(17)
Fig. 10
Fig. 10
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Variation of SO2eq emissions/capacity−0.19 with head for dam-toe SHP schemes.

The observed correlation, where SO2eq emissions per unit capacity raised to the power of − 0.19 decrease with increasing head, suggests that higher heads are associated with reduced emissions. This reduction can be attributed to the greater energy generation efficiency at higher heads, which allows for more hydraulic energy to be harnessed from each unit of water. Consequently, this improved efficiency reduces the reliance on extensive infrastructure and civil works, which are typically significant contributors to emissions during the construction and operational phases. Therefore, dam-toe SHP schemes not only enhance energy output but also minimize environmental impacts as head height increases.

The final correlation for estimation of SO2 emissions for dam-toe schemes with head varying from 10 to 40 m is expressed as:

$$SO_{2} emissions = 0.554 \times H^{ - 0.09} \times C^{ - 0.19}$$
(18)

Similarly, the same method is used to develop correlations for dam-toe schemes with head varying from 40 to 258 m. The variation of SO2 emissions in g-SO2eq/kWhe with capacity in kW is shown in Fig. 11 which also gives the following power law relationship. Figure 11 demonstrates that emissions decrease with higher capacity installations in dam-toe schemes, especially in higher head ranges (40–258 m), highlighting the importance of maximizing capacity to achieve lower emissions per unit of electricity generated.

$$SO_{2} emissions = B_{0} \times C^{ - 0.14}$$
(19)
Fig. 11
Fig. 11
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Variation of SO2eq emissions with capacity for head range 40–300 m for dam-toe schemes.

Again, the coefficient B0 will depend on any other parameter i.e. head. Figure 12 shows the variation of SO2 emissions/capacity−0.14 with head. Figure 12 emphasizes that higher head dam-toe schemes result in lower emissions per unit capacity, making them an efficient option for minimizing environmental impact in small hydropower projects. The final correlation for estimation of SO2 emissions for dam-toe schemes with head varying from 40 to 258 m is expressed as:

$$SO_{2} emissions/C^{ - 0.14} = 0.454 \times H^{0.033}$$
(20)
$$SO_{2} emissions = 0.454 \times H^{0.033} \times C^{ - 0.14}$$
(21)
Fig. 12
Fig. 12
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Variation of SO2eq emissions/capacity−0.14 with head for dam-toe schemes.

Results and discussion

The results of this study indicate a clear trend in the emissions of sulphur dioxide equivalents (SO2eq) across various types of SHP projects. A notable finding is that larger projects generally have lower emissions per unit of electricity generated (g-SO2eq/kWh), owing to the economy of scale. Additionally, the head of the SHP projects was found to significantly affect emissions. While run-of-river projects displayed increased emissions with higher heads, canal-based and dam-toe projects experienced the opposite, with higher heads leading to lower emissions.

The findings of this study align with previous research on the environmental impacts of hydropower. Keller et al.14 found that integrating renewable energy sources reduces environmental degradation, supporting our conclusion that SHP projects contribute to lower acidification potential compared to conventional systems. Similarly, Hondo9 emphasized the lower greenhouse gas emissions from hydropower, which complements our results showing that SHP projects, while not emission-free, produce significantly fewer acidifying pollutants. Additionally, Yang et al.2 highlighted how site-specific factors influence acidification potential, a key aspect also observed in our study where head height and project type significantly affect SO₂ emissions. By correlating emissions with project parameters, this study contributes to the broader understanding of reducing the environmental impact of hydropower projects.

To validate the developed correlations, a comparison between the actual and predicted SO2eq emissions was made. As shown in Fig. 13, the actual SO2eq emissions from run-of-river, canal-based, and dam-toe SHP schemes closely align with the values predicted by the statistical models. The deviations between the predicted and actual values are within an acceptable range of 14%. These deviations are likely due to site-specific factors such as local geography, civil works, and the type of electro-mechanical equipment used. This validation supports the reliability of the developed models and demonstrates their applicability in accurately predicting emissions for future SHP projects. The findings of this study provide valuable insights into the acidification potential of SHP projects in India, which has been a relatively unexplored area. The study highlights the importance of optimizing project design to reduce environmental impacts, particularly in regions where SHP projects are expected to grow. The study also finds that higher head heights in canal-based and dam-toe schemes further improve efficiency and reduce emissions. The implications for SHP development in India suggest prioritizing larger, high-head projects to minimize environmental impact. Additionally, optimizing design and using the study’s predictive models can guide sustainable SHP development and help meet India’s energy and environmental goals.

Fig. 13
Fig. 13
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Comparison graph between the actual and predicted value of SO2eq emissions for different small hydropower (run-of river, canal based, dam-toe) schemes.

The study’s findings provide key insights for policymakers involved in SHP project development. Based on the correlations between project parameters and SO₂ emissions, the following recommendations can help guide the selection of environmentally sustainable SHP projects.

Policies should prioritize larger SHP projects, within the 25 MW threshold, as they generally have lower SO₂ emissions per unit of electricity. For run-of-river schemes, it is important to optimize head height, as higher heads may lead to increased emissions, whereas canal-based and dam-toe projects should aim for higher heads to reduce emissions. Additionally, environmental assessments should integrate predictive models from this study to guide project approvals based on minimizing acidification potential.

Conclusions

This study successfully quantifies the life cycle acidification potential (SO2eq emissions) of various Small Hydropower (SHP) projects in India using an economic input–output life cycle assessment (EIO-LCA) methodology. A key contribution of this research is the development of predictive models that estimate SO2eq emissions based on specific project parameters such as capacity, head, and scheme type. These models fill a significant gap in the existing literature by providing a nuanced understanding of SHP environmental impacts in India.

Key findings

  • Capacity and Emissions: The analysis demonstrates that larger SHP projects typically exhibit lower emissions per unit of electricity generated, benefiting from economies of scale.

  • Head Height and Emissions: The emissions are influenced by the head height, with canal-based and dam-toe projects showing reduced emissions at higher heads, highlighting the efficiency gains from better hydraulic designs.

Future directions

  • Technological Innovations: Further research is suggested to incorporate emerging technologies like artificial intelligence and machine learning, which could refine SHP design and operational strategies to minimize environmental impacts.

  • Materials and Construction: Investigating the use of sustainable, low-emission construction materials could help decrease the overall environmental footprint of SHP projects.

  • Climate Change Impact: Future studies should consider the potential impact of climate change on hydrological conditions, which could affect both the feasibility and the environmental performance of SHP projects.

  • Policy Integration: It’s recommended that the models developed through this study be integrated into national and regional policy frameworks to support the sustainable expansion of SHP projects.

These findings and recommendations are aimed at guiding future SHP developments in a manner that aligns with India’s sustainability goals, ensuring that the environmental impacts are adequately managed while continuing to meet the growing demand for renewable energy.

The life cycle inventory analysis for the Rayat Mini Hydel Project (3000 kW, Run-of-River) and the Sadani Mini Hydroelectric Project (2 × 500 kW, Dam-Toe) is based on the economic input–output life cycle assessment (EIO-LCA) method.

For the Rayat Mini Hydel Project, main civil works accounted for ₹65.845 million, with significant emissions primarily from construction. Electro-mechanical equipment mainly including turbines and generators, ₹60.98 million further contributing to emissions.

In the Sadani Mini Hydroelectric Project, the main civil works cost was ₹17.922 million, and the electro-mechanical equipment cost mainly was ₹20.852 million. Due to its smaller capacity, this project had lower emissions compared to Rayat, especially in the operation and maintenance phase.

Overall, the civil works and electro-mechanical equipment phases contribute the most to life cycle emissions, with the larger Rayat project exhibiting higher emissions.

S.No

Component

Cost in Rs 2004-05 (106)

Cost in US $ (2002) (106)

1 (Rayat)

2 (Sadani)

1 (Rayat)

2 (Sadani)

 

Civil works

1

Construction

65.845

17.922

5.82

1.58

2

Erection

10.59

1.92

0.94

0.16

3

Penstock

4.971

4.47

0.44

0.39

4

Coffer dam

–

0.590

–

0.053

 

Electro-mechanical equipment

1

Turbine and generator

60.98

20.852

5.39

1.84

2

Control panel

18.79

–

1.66

–

3

Station auxiliary

13.58

0.836

1.21

0.073

4

Transformer and switchyard

10.50

2.164

0.93

0.19

 

Operation and maintenance per year

1

Civil works

2.4422

0.747

0.22

0.06

2

Electro-mechanical equipment

3.1157

0.716

0.27

0.063

3

Others

1.710

0.492

0.15

0.043

This study uses economic data from 2004-05 (INR) and 2002 (USD) because more recent, comprehensive data was not readily available for the small hydropower projects studied. Updating to current data would require adjustments for inflation, exchange rates, and material costs, which were beyond the scope of this work. While this limits the precision of the analysis for today’s context, it ensures consistency with past assessments. Future studies could address this by incorporating updated cost data.