Uncertainty Factors Influencing Hydroelectric LCA Studies: A Review

Abstract

Despite the increase in research on Life Cycle Assessment (LCA) of Hydroelectric Power Plants (HPP) there are issues that need to be better discussed. This review aims to discuss factors that influence HPP LCAs such as: indirect emissions, different stages of HPPs (construction, operation, and decommissioning), scale/productivity of HPPs, types of projects (reservoir and run-of-river) and use of the ground. Most of the results obtained by HPP LCAs indicate that the construction phase is the most influential phase for indirect emissions due to the use of steel and concrete. The comparison of the HPP’s LCA results with the LCA of other energy sources indicates that for the analyzed category Global Warming Potential (GWP), the HPPs present a good environmental performance considering the quantified emissions, their productivity and useful life. The present review highlights some uncertainty factors that influence HPP LCA studies and cites the need to carry out future studies on the environmental impacts of HPPs including these factors.

1. Introduction

A major challenge in today’s world is to meet the demand for increased energy production considering environmental factors. The concern with sustainability in the sector leads countries to sign agreements for the replacement of energy production from non-renewable sources (coal, natural gas, oil and derivatives, uranium) by renewable ones (charcoal, hydraulic, wind, solar photovoltaic, biomass). Brazil has a privileged position in terms of energy production from renewable sources. According to the report [1] the share of renewables sources in the Brazilian electricity matrix reached 84.8% in 2020, against 23% in the rest of the world and 27% in OECD countries. In Brazil, hydraulic energy is positioned as the main generating source, where 67% of the energy generated in 2021 comes from hydroelectric plants [2]. Although Brazil occupies a privileged position in the renewable energy sector, in 2020 Brazil lost its place to China, which added 12.6 GW of hydroelectric capacity and regained its leadership. Many HPPs are being implemented, especially in China, India, and in northern Brazil, with the objective of increasing renewable energy production and meeting the growth of demand. Considering the above, it is important to assess the environmental impact caused by hydroelectric plants, since large hydroelectric plants are part of the energy matrix scenario in Brazil and in the world [3].

There are many factors that influence the analysis of HPP emissions, which is why it becomes a complex task. One of the ways to carry out this analysis, considering these factors, is through the Life Cycle Analysis (LCA). LCA is a methodology that makes it possible to analyze environmental damage and is defined in ISO [4] as: the study of environmental impacts throughout the life of a product from obtaining the raw material, passing through production, use and ending with discard it. LCA methodology approach holistically the entire process, identifying the most significant impacts, pointing out improvements and at what stages they can be applied. In this way, it prevents damage from spreading along the stages, causing a chain effect from one environmental problem to another or from one region to another [5]. According to ISO [4], the methodology for LCA comprises four steps.

1st) Objective and scope definition: the objective and scope must be clearly defined to ensure that no relevant part is omitted.

2nd) Life Cycle Inventory (LCI): in this step, data collection and calculation procedures are carried out to quantify the inputs and outputs relevant to the study of the system, as defined by the objectives and scope in the previous step.

3rd) Impact Analysis (LCIA): the third phase of the LCA aims to assess, quantify, and convert the environmental loads caused by the inputs (raw materials and energy) and outputs (waste and other emissions) of the system, into impacts on health, the environment, and the use of natural resources. Mandatory activities at this stage are the selection of impact categories, the definition of category indicators and characterization models, in addition to the classification and characterization of data. An impact category is a class representing environmental problems, such as global warming, acidification, human toxicity, etc.

4th) Interpretation: the last phase of the LCA aims to identify the significant environmental issues present in the results of the previous phases; evaluate the methodology, check the consistency, completeness and sensitivity of the data and propose recommendations for improvements in the performance of the system.

The LCA methodology is standardized [5]. Currently, LCA is being used for decision making in choosing the best option between products and processes in many contexts such as chemical engineering [6], in the use of disposable packaging [7], in agriculture [8], in the transport of products [9], in building construction [10], and frequently in the production of energy.

Therefore, analysis of emissions in energy production, through the LCA methodology, can be carried out in different contexts, such as: In Queiroz et al. [11] LCA was carried out in the process of producing biofuel from a palm tree in the Brazilian Amazon. Matuszewska [12] identified the configuration of geothermal systems using LCA. References [13, 14, 15, 16, 17, 18, 19] applied LCA to study the environmental damage of photovoltaic systems. Brizmouhun et al. [20] carried out the LCA study with the aim of identifying the environmental damage of power generation in Mauritius. Puettmann and Wilson [21] compared the cradle-to-gate total energy and major emissions for the extraction of raw materials, production, and transportation in wood products industry in United States. In Piasecka et al. [22] was used LCA to analyze and compare the environmental impact of offshore and onshore wind farms, considering 2 MW installations, which are the most frequent in Central and Eastern Europe.

In relation to HPPs, the analysis of emissions produced by the LCA methodology is accompanied by many uncertainties. Each plant has specific characteristics, such as location, size, type, productivity, land use, among others. All this influence the analysis causing a need for a study with well-defined methodology. HPP are becoming the solution to the problem of energy demand in many countries in the world, justifying the importance of using LCA to analyze the environmental impacts caused by this type of plant [23].

We can cite some examples such as: Pang et al. [24] compared the environmental impacts of a small HPP in China with similar ones located in other parts of the world, while Suwanit and Gheewala [25] evaluated the environmental impacts through the LCA of mini HPPs that generate electricity in Thailand. The biggest energy producer in the Nordic countries is Vattenfall AB. Vattenfall’s main markets are Sweden, Germany, the Netherlands, Denmark, and the United Kingdom. Vattenfall [26] presents the LCA study carried out by Vattenfall, evaluating the environmental impacts of different energy sources in the Nordic countries. Santoyo-Castelazo et al. [27] analyzed the environmental impacts of HPPs in Mexico and other energy sources that are part of the supply network and presented a comparison between them. Flur and Frischknech [28] conducted a study which had the objective of describing the environmental impacts of construction, operation and decommission of HPPs with the focus on Switzerland, extrapolated to other regions such as Brazil. Hanafi and Riman [29] evaluated life cycle of a mini HPP in Simalungun - Indonesia and showed that the most evident impacts are carcinogenic, and eco-toxicity in marine and freshwater biota generated from the construction of the mini HPP.

After this preliminary study, we observed that there are extensive possibilities of applications for LCA methodology. An approach to the most influential aspects for analysis using LCA is the objective of this work, and we consider it a contribution to the current state of the art. The analyzed aspects include: the different phases of the LCA, the importance of indirect emissions in studies of hydropower reservoirs, the scale/capacity ratio, land use. We added a discussion on the type of run-of-river hydropower and the challenges of using more sustainable technologies.

The present work demonstrates the importance of the LCA methodology when considering all phases of the life cycle of HPP and highlights specific characteristics of HPPs when their environmental impacts are analyzed. Brazil is a developing country, and, like other similar countries, it has an unexplored hydraulic potential. HPPs can become one of the best solutions to meet energy growth and demand. However, the challenge for these countries is to study and analyze the best way to produce energy without causing major impacts on the environment. And the LCA methodology has been an aid tool for such studies.

2. Methodology and materials

The methodology used in this paper included six steps as in Geller et al. [23]:

• Definition of the objective and delimitation of the research scope: The objective of the research is to present some characteristics of the HPPs that influence the analysis of environmental impacts through the LCA. This review includes comparative analyzes of the LCA of HPPs and another energy sources.

• Selection of sources in the literature: The selection of research sources was conducted with the following criteria: 1st) LCA reports published between 2000 and 2021, containing analysis of the environmental impact of HPPs and other energy sources; 2nd) studies that identified influencing factors on the LCA of HPPs. The search was carried out using the keywords: LCA, Hydropower, GWP, emissions, following this order of priority.

• Definition of factors that most influence HPP’s LCA studies: There are many factors that cause uncertainties and influence LCA studies for hydroelectric plants. To carry out this study, the following factors were selected: Indirect emissions, types of HPPs (reservoir and run-of-river), the different phases of the life cycle, land use, location, scala/productivity.

• Review including only HPP’s LCA: A review of articles related to HPP’s’s LCA was necessary to compare the different contexts, highlighting a study carried out in a HPP’s in northern Brazil. The impact category highlighted was the Global Warming Potential – GWP, which according to Acero et al. [30] “expresses climate change referring to the global temperature caused by greenhouse gases released by human activity, measured in the reference unit kg of CO₂ equivalent (kg CO₂ eq)”.

• Countries like China, Brazil, USA, Canada, and Russia have a high production of electric energy through HPPs [31]. However, there are few studies on their emissions analyzed through LCA, leading to the need to include here some reviews carried out by other authors. With the results produced by other works cited in this review, it was possible to make comparisons between hydroelectric plants with different characteristics (in Section 4) and between hydroelectric plants and other energy sources (in Section 3).

• Recognition of HPP LCA uncertainties: Compiling the results of the reviewed works and recognizing the uncertainties related to HPP LCA constitutes a significant step towards the objective of the study.

• Identification of challenges for future research: to highlighting points to be better studied and which constitute challenges for future research is one of the characteristics of the review study. The conclusion includes the authors’ view on this challenge.

3. A comparison between hydropower and other sources using LCA

It is known that different energy sources have their emissions influenced by their characteristics. To recognize the environmental feasibility and sustainable character of each one, it is necessary to carry out a quantitative comparison of these emissions. And one way to make this comparison is through results from LCA. This section also includes a comparison with the LCA study carried out by the authors at the Curuá-Una HPP, located in the Amazon in northern Brazil.

The electricity and heat distribution company Vattenfall presented in Vattenfall [26] the LCA data for nuclear, hydro, wind, solar and biomass energy. Vattenffal is one of the biggest producers of electricity and heat in Europe. Its most important markets are Sweden, Germany, Holland, Denmark and the United Kingdom. The methodology used pela Vattenfall divided the life cycle into 4 stages, namely: (i) production and transport of fuel; (ii) plant operations; (iii) infrastructure that includes construction, maintenance and decommissioning of the plant and (iv) radioactive waste management. Table 1 presents the main energy production technologies and their respective contributions. According to Vattenfall [26], the largest amount of emissions is generated by the coal plant in the operational phase. This reinforces the conclusion of the analyzes that the emissions from the construction phase are higher for plants that do not burn fuel but use renewable resources such as HPPs and wind farms. For biomass and coal-burning fuel plants, emissions are highest in the operational phase [39].

SOURCE	Coal	Oil	Natural gas	Biomass	Solar PV	Wind	Nuclear	Hydro
	(kgCO2 eq/MWh)
[32]	1230	1213.4	855	97.3	76.3	46.4	17.1	13.2	China (Ecoinvent)
[26]						15.6	4.44	7.26	Nordic Countries (Electricity mix)
[33]	600–1050	530–900	380–1000	8.5–120	13–190	3.0–41	3.0–35	2.0–20	Literature review
[20]	1444	754		29				8.6	Mauritius (Electricity mix)
[31]	888	733	499	14.0–650	9.0–300	8.0–124	24.2	2.0–75	Literature review
[34]	1118		514			16.9–30.4	39.5	27.7–43.8	Literature review
[35]	960–1050	778	443	118	13	9	15	2.0–15	Literature review
[36]	975.2	742.1			53	29.5	24	11.3	Japan
[37]	900–1200	790–900	400–500			4.6–55.4		0.2–152	Literature review
[38]						28.6 ± 3.2	12.4 ± 1.5	3.5 ± 0.4	China

Table 1.

Life cycle GHG emissions (kgCO₂ eq/MWh) of different technologies.

A literature review with 167 LCA studies was performed by Turconi et al. [33]. GHG emissions have been identified for the most diverse energy sources, including coal, lignite, natural gas, oil, nuclear, biomass, hydro, solar PV and wind. Table 1 presents the results for each technology and points out that coal, lignite, natural gas and oil have the greatest impact compared to hydro, nuclear and wind power.

In the Republic of Mauritius, the main source of energy is fossil fuels and Brizmouhun et al. [20] used the LCA methodology to compare GHG emissions from these plants and eight other hydroelectric plants, of which 4 are reservoir and 4 are run-of-river plants. The results presented in Table 1 reinforce the higher GHG emission for fossil fuel plants compared to HPPs.

In the work of Gagnon et al. [35] several energy sources were analyzed with the LCA methodology. A point to emphasize in this study is that the results presented the lowest impact for run-of-river plants followed by wind, solar photovoltaic and nuclear plants. The HPPs with reservoir had the worst performance.

The environmental impacts of electricity generation in China were analyzed by Feng et al. [32] through LCA, using data from eight different technologies. For the analysis of energy that has oil, natural gas, hydro, and photovoltaic energy as sources, the Ecoinvent base processes (repository with more than 18,000 datasets) were used. To analyze the impacts produced by nuclear, coal, biomass and wind energy, the studied plants are in China [18]. The study shows that CO₂ emissions from fossil fuel-based technologies are much higher than emissions from renewable sources of energy when analyzed over the life cycle (Table 1).

The review of Amponsah et al. [31] includes 79 studies of LCA. The objective was to compare renewable energy sources (RETs) such as photovoltaic, wind, biomass, wave energy and hydropower with conventional energy sources such as oil, lignite, and coal (Table 1).

The research carried out by Hondo [36] pointed out that one of the factors that can reduce the impacts on energy production is the choice of technology, such as the material used in photovoltaic panels. The author proved with his study there was a reduction from 29.5 CO₂-eq/MWh to 20.3 CO₂-eq/MWh for wind energy and 53.4 CO₂-eq/MWh to 26 CO₂-eq/MWh for PV (Table 1). For this analysis, it measured the emissions of nine types of energy production technology, among them: nuclear, hydroelectric, geothermal, wind, solar-photovoltaic (PV)coal, oil, liquefied natural gas (LNG) and LNG combined cycle.

Littlefield et al. [34] included the environmental profile in their research on energy production feasibility analysis. The environmental profile uses LCA and assesses resource consumption, emissions to water and air, solid waste, and land use. Seven technologies were analyzed, among them: hydroelectric, wind, nuclear, natural gas, coal and biomass co-burning, geothermal and solar thermal resources. The results pointed to the wind farm with the best environmental performance (Table 1).

In Like et al. [38] wind, nuclear and hydraulic plants were evaluated with LCA, considering all stages of the life cycle. Table 1 shows that wind power has a greater environmental impact than nuclear and hydro power. Considering the global warming potential, wind energy produces 28.6 ± 3.2 g CO₂-eq/kWh of GWP100 throughout its life cycle, which is higher than nuclear energy (12.4 ± 1.5 g CO₂ -eq/kWh) and hydroelectric (3.5 ± 0.4 g CO₂-eq/kWh).

We can see that the results presented in Table 1 show the different approaches in the use of LCA to measure the environmental impacts on energy production. Knowing the characteristics of these plants that influence the different results constitutes a relevant study to aid in decision making when seeking sustainability.

The LCA methodology used to compare various energy sources requires a standard of functional unit. This allows the productivity of each energy source to be considered. In the case of the results presented in Table 1, the functional unit is kgCO₂-eq/MWh. This standard is important because, like hydroelectric plants, which feature high productivity and longer lifespan than other technologies, their environmental impact is diluted both in the amount of energy produced and in its lifespan. In other words, to produce the same amount of energy a wind farm needs many resources (panels, batteries, inverters) that use raw materials (inputs such as metals and energy), contributing to the various categories of environmental impact [16].

Table 1 shows that the greatest variation in GHG emission quantification is for HPPs (0.2–152 kgCO₂-eq / MWh) and biomass (8.5–650 kgCO₂-eq / MWh), a result discussed in Topic 5.6.

4. LCA of HPPs: comparing different characteristics

To further complement the study an analysis of LCAs limited to HPPs with different characteristics was conducted including different phases of LCA, size of plants, type of plants (reservoir or run-of-river) and land use.

When comparing LCAs done for different HPPs, to obtain better interpretation and analysis, it is very important to consider the objective of each study as this leads to specific results. Some studies consider only the GWP factor [16, 27, 40], whereas others present the total of emission for some factors [37, 41]. There are studies that review several LCAs done by different authors and compare them, such as the study by Refs. [33, 37]. Research in Refs. [24, 39] evaluated the most representative impact categories for each stage of LCA applied to hydropower. The review in this session considered analyzes only of HPPs with LCA methodology, independent of other sources of energy production. All were standardized with the functional unit kWh or MWh, and different results for these analyzes are shown in Table 2.

	Total (kg CO2-eq/MWh)	Study Site/Type	Type
[24]	28.4	China	dam-toe-based
[40]	32.23–35.35	India	canal based
[40]	11.91–31.2	India	dam-toe-based
[25]	17.62	Thailand	run-of-river
[29]	1.2	Indonesia	run-of-river
[42]	52.7	Thailand	run-of-river
[43]	5.43–8.93	UK	run-of-river
[44]	4.33	Brazil	reservoir
[45]	13.06–19.12	India	run-of-river
[46]	21–40.63	Literature review	reservoir
[46]	3.0–47	Literature review	run-of-river
[46]	256.63	Literature review	pumped storage
[33]	2	Literature review	run-of-river
[33]	15	Literature review	reservoir
[37] *	4.0–152	Literature Review	reservoir
[37] **	0.2–11.2	Literature Review	reservoir
[37]	4.9	Literature Review	run-of-river
[47]	>150.0	Literature Review	reservoir
[47]	4.0–14.0	Literature review	run-of-river

Table 2.

HPP- life cycle GHG emissions (kg CO₂-eq/MWh).

*

Including gross emissions from flooded land.

**

Excluding emissions from flooded land.

Varun and Bhat [40] studied emissions from 6 hydropower plants located in India, of which three are canal-based projects and three are dam-based projects. The results of this study, in relation to GHG emissions, are shown in Table 2. The specific characteristics of each HPP such as capacity, type of technology, location and head size produce variation in the values found in the analysis performed with LCA.

Five run-of-river mini HPPs, located in Thailand, were the object of study in Suwanit and Gheewala [25]. Considered useful life was 50 years and functional unit was 1MWh. For analysis of the results, the averages of the five mini HPPs were calculated and are shown in Table 2. Reproducing the authors’ conclusion “the main contributors to the impacts are the materials used in construction such as gravel, sand, cement, steel, iron, copper and the energy used by the equipment” [25].

A review carried out by Raadal et al. [37] compared the emissions caused by 28 reservoir-type HPPs and 11 run-of-river HPPs, as shown in Table 2. Among the evidence of the result is the importance of considering the flooded area as a significant factor impact on the environment. The reservoir filling stage is highlighted as the biggest contributor to GHG emissions, even surpassing the construction stage.

In Hanafi and Riman [29] the LCA methodology was used for the analysis and a HPP located in Simalungun in Indonesia. The system operates with two generators, it is of the run-of-river type, has a capacity of 9 MW and productivity of 8MWh, registering 80% efficiency. Considering a useful life of 50 years, the main stages defined by the methodology were: pre-construction, construction, and operation. This study reinforced what other results have also shown, the greatest impact to the construction phase. However, the most influential categories for this phase are ecotoxicity and the release of carcinogenic materials. The authors justify this result using materials such as steel, nickel, and concrete for the construction of the duct. They emphasize that the amount of GHG emission (1.2 kg CO₂-eq/MWh) is lower than the categories mentioned above (Table 2).

The review carried out by Kumar et al. [47] presents estimates of GHG emissions from reservoir and run-of-river plants, reporting values ​​that exceed 150 kgCO₂-eq/MWh for the former and a range of 4–14 kg CO₂-eq/MWh for the last. The authors emphasize that this different behavior in terms of emissions is due to the use of land by reservoir-type HPPs and that all phases can contribute to this, due to the emission of methane with the decomposition of vegetation (construction and operation phases) and the sludge deposited throughout the plant’s useful life (decommissioning phase).

The study by Pascale et al. [42] compared the emissions produced by a small dam in a rural community in Thailand with the emissions from larger dams, with the objective of finding the best alternative for the electrification of these communities. For this, it carried out the LCA of a small 3 kW system considering its entire life cycle, that is, from the cradle to the grave. The results presented in Table 2 reflect the trend found in the literature, that is, larger HPPs, due to their higher productivity, have a lower environmental impact per kWh produced, compared to smaller HPPs.

Itaipu HPP plant is a binational power plant (Brazil-Paraguay), located on the Paraná River, on the border between these two countries. Until 2012 it was considered the largest hydroelectric plant in the world, with a capacity of 14,000 MW. Ribeiro and Silva [44] used the LCA methodology to make an inventory of material and energy consumption, atmospheric emissions and land use and transformation. According to the authors, the good environmental performance (4.33 kg CO₂-eq/MWh) obtained as a result of the study (Table 2) is justified by the high productivity of this large hydroelectric plant.

Three run-of-river HPPs located in the United Kingdom (UK) were analyzed in Gallagher et al. [43]. Each one had a capacity of 650 kW, 100 kW and 50 kW and the result of the study shows emissions of 5.43 kg CO₂-eq/MWh, 7.39 kg CO₂-eq/MWh and 8.93 kg CO₂-eq/MWh respectively (Table 2). According to the authors, there are few LCA studies for small (~100–1000 kW) and micro (~10-100 kW) HPPs that highlighted the importance of knowing their emissions because hydroelectric plants have significant growth in some regions (Table 2).

Kadiyala et al. [46] created an index categorizing HPPs according to capacity (micro, small and large) and type (impoundment, diversion, pumped storage, miscellaneous hydropower works). The mean GHG emission resulting from small HPP dams was higher than large hydropower dams of the same type. The highest average emissions were found for pumped storage (Table 2).

Pant et al. [45] selected three small run-of-river hydropower plants located in India with capacities of 30 MW, 33.33 MW and 51 MW. The analysis was carried out using the LCA methodology, considering, in addition to the capacity, the head of the power plants. The results show that emissions are lower when there is higher productivity, on the other hand, they increase with the increase in the head of the plant.

4.1 LCA of HPPs detailed in different phases

Different stages of the life cycle of a HPP produce emissions of the most diverse natures and quantities. In this session, we present some studies on HPP LCA found in the literature that confirm this statement.

Pang et al. [24] confirmed in their research (Table 3) using the LCA methodology, that the construction phase in the life cycle of a HPP is the one with the greatest environmental impact due to the emission of GHGs. They analyzed the Guanyinyan power plant, in northeast China, which has 2 turbines, a capacity of 1.6 MW and an average annual production of 6.28GWh. They used the 1 MWh functional unit and considered the plant’s useful life to be 30 years. To complement the research, they carried out a sensitivity analysis, increasing and decreasing inputs by 10%, and studying the consumption of materials such as steel, cement, and energy. They obtained ±6.8% in the results and concluded that “it is necessary to optimize structural designs using new materials and best practices to reduce the level of emissions in relation to the energy generated [24].”

	Total	Construction	Operation	Transportation	Maintenance	Disposal
	(kg CO2-eq/MWh)
[24]	28.4	27.3		0.2	0.9	0.0
[39]	5.4	4.8	0.11	0.46		0.0
[48]	78.1	2.5 ± 0.5	75 ± 0.5			0.55–0.65

Table 3.

HPP - life cycle GHG emissions (kg CO₂-eq/MWh) – Phases.

The LCA study carried out in Geller and Meneses [39] specified the emissions from the Curuá-Una hydroelectric plant located in the Amazon region in its different phases (Table 3). Zhou [48]​ included in his study the HPP of Nam Theun 2, located in southeastern Laos with a capacity of 1070 MW (Table 3), also in different phases.

In Zhou [48] ​they pointed out that the operational phase of the plant is the phase had the greatest impact due to the emissions produced by the reservoir. The gases carbon dioxide is produced by aerobic decomposition and methane by anaerobic decomposition and are the gases produced in greater quantity in the plants located in tropical regions. Methane accounts for 85% of total emissions at warmer temperatures.

These three studies are used here because they relate a high amplitude in the results that can be attributed to the different forms of application of the LCA methods.

In Pang et al. [24] the LCA analysis of a small HPP (1.6 MW) with a useful life of 30 years was performed. In the other case Geller and Meneses [39] the HPP has a capacity of (30.3 MW) and the analysis was performed for a useful life of 100 years. The results as shown in Table 3 were quite different, justified by the fact that the greater capacity and longer useful life results in greater productivity, causing the quantities to be diluted in relation to the production factor (emission/production factor). And as already mentioned, the high value of the operation phase in Zhou [48] is due to the inclusion of direct emissions of methane and carbon dioxide gases, produced by the decomposition of biomass in the flooded area of ​​the reservoir. It is worth mentioning that in Refs. [24, 39] only indirect emissions were analyzed.

Geller and Meneses [39] investigated the environmental impacts of the construction, operation and deactivation phases of the HPP Curuá-Una plant located 70 km from the city of Santarém, in the northern region of Brazil, that is, in the Brazilian Amazon. The HPP was inaugurated on 08/19/1977 and at the time of the study (2016), the plant operated with three turbines with an operating capacity of 30.3 MW and with an efficiency rate of 92.89%, being considered by Brazilian standards as a large plant. The investigation was carried out with real data and data from the Ecoinvent database and the categories analyzed include:

• Global Warming Potential (GWP),

• Acidification Potential (AP),

• Abiotic Depletion Resources (ADP),

• Freshwater Aquatic Ecotoxicity Potential (FAETP)

• Human Toxicity Potential (HTP),

The results presented in Table 4, show the four phases of the life cycle analyzed in Geller and Meneses [39]. Among the categories analyzed, the most affected are HTP, GWP and FAETP and the stage with the highest emission is the construction phase. Fossil energy was used in this phase, which is a major contributor to these impact categories. The construction phase has emissions related to all impact categories and among the main contributors to this fact is the use of concrete used in the dam and steel used in infrastructure and equipment such as generators and turbines. It is noteworthy that the methodology used did not include direct emissions, that must be measured directly in the reservoir, as already mentioned, so the low results for the operation phase with the appropriate emissions of CH₄ and CO₂ are noted.

Impact category	Reference unit	Complete life cycle	Construction	Operation	Transportation	Decommission
AP	[kg SO2-eq]	0.0223	0.0189	0.0009	0.0025	−0.000
GWP 100y	[kg CO2-eq]	5.4659	4.8922	0.1121	0.4679	−0.0065
ADP	[kg Sb eq]	0.0312	0.0247	0.0033	0.0032	0
FAETP 100y	[kg 1.4-DCB eq]	2.4505	2.2971	0.1169	0.0371	−0.0007
HTP 100y	[kg 1.4-DCB eq]	7.2858	6.4277	0.6267	0.2345	−0.0031

Table 4.

Contribution of Curuá-Una life cycle phases to each impact category [39].

All results presented confirm the need for specific studies in each context and for each objective, since the characteristics of each plant are different, and the objectives of the studies require adequacy of the LCA methodology. According to Geller and Meneses [39] “a direct comparison between HPPs is difficult and should be made carefully because HPPs are highly site-specific, and their environmental impacts are associated with their different characteristics.”

5. Discussion

This session discusses issues regarding the specific characteristics of hydropower that impact the Life Cycle Analysis results when analyzing emissions. The diversity of studies and contexts in which this methodology is applied was observed in the previous sessions, generating variation in the results.

5.1 Direct and indirect emissions of HPPs

Emissions produced by the generation of energy are classified in two ways: indirect and direct [28]. The former are the emissions caused by the construction, implantation, and deactivation of the plant, which according to Steinhurst et al. [49] include infrastructure of roads and transmission lines, the work of implantation, manufacture of materials, transport, disposal of material, etc. According to Dones et al. [41], the largest contributor to GHG emissions in this category includes cement production and the use of diesel for electricity. Hanafi and Riman [29] state that “the biggest contributing factor related to the infrastructure for the emission of GHGs is the production of concrete and the transport of stones for the construction of dams and tunnels”. Indirect emissions can represent less than 20% of generating plants using fossil fuel and more than 90% of generating plants using renewable and nuclear sources [49].

And direct emissions are those resulting from the phase in which the plant is in operation, such as burning of fuel used to operate the plant, use of land/flooded area, goods and services for plant operation, etc. In relation to the direct emission produced by the hydroelectric plants, the decomposition of the biomass of the flooded soil of the reservoir is one of the most significant [29]. The impacts caused by these emissions are classified according to the scope of the area they affect in global impact, regional impact and local impact, that is, some may have a significant impact in one region, but not in another. For example, global warming, depletion of the ozone layer, biotic and abiotic resources caused by emissions from a particular location, can cause global impacts; land use will be responsible for local impacts [4].

Hydroelectric plant is a renewable energy source that is not entirely clean, because if considered throughout its life cycle, it has direct and indirect contributions to the production of GHGs. Some studies in Refs. [28, 49, 50, 51, 52] are concerned to show that the idea that hydroelectric plants have low levels of emissions is somehow wrong. They argue that emissions can sometimes exceed those of plants that use non-renewable raw materials, such as fossil fuels, in their implementation phase and in some cases also in the operational phase, as described by Refs. [53, 54]. The review presented in this chapter is not intended to provide details of these results.

5.2 Reservoir HPPs and indirect emissions

The consumption of concrete, steel, energy, fuel, and other materials for the construction of the plat, in addition to the use and transformation of the soil, are the most responsible for the indirect emissions that impact the construction phase.

Geller and Meneses [39] state that the construction stage at the Curuá-Una HPP was the most critical phase in relation to the indirect emissions caused (Table 3), due to the inputs for deploying the HPP, which is corroborated by other authors [24, 41, 49, 55]. Pang et al. [24] concluded that steel and concrete are the largest contributors among the materials used as inputs due to their production chains.

References [37, 41] reinforce that cement production, the use of diesel for electricity and the transport of stone for construction of dams and tunnels are major contributors to GHG emissions. 90% of emissions from renewable plants come from the construction phase [49]. Vattenfall [26] say that plants that use renewable resources for energy production (hydro, solar, wind), that is, do not burn fossil fuel, have their most impacting indirect emissions in the construction phase.

5.3 Scale/capacity of the hydropower plants

According to Refs. [24, 35, 41, 44] the capacity and scale of projects also influence GHG emissions, stating that smaller systems have worse environmental performance than systems with greater capacity. This is since the higher productivity of the latter, reduces the ratio between the emission and the energy produced in MWh during the useful life of the plant. Zhang et al. [56] carried out LCA of two HPPs in China, with 44 MW and 3600 MW, and the GHG emissions were 44 and 6 kg CO₂-eq/MWh, respectively.

We can explain by observing the statements of these authors that the ratio between the impact per MWh and the higher productivity of the plant, considering its long-life cycle, are factors that can make the HPPs the more environmentally viable option to meet greater demands, because it dilutes the amount of impacts over its useful life. However, the need to serve small populations or rural communities located at great distances from large HPPss, small plants are still a very viable alternative for energy production, having better environmental performance than fossil fuel plants, that is often used in this context.

Environmental impacts are influenced by several factors in addition to the productivity of the plant, as already seen. Therefore, the analysis of the LCA of HPPs with different scale and capacity must be carried out with great care, even when the values are parameterized.

5.4 Land use and land use change (LULUC)

A highly discussed issue with respect to LCA of HPPs is to land use change and Land Use Change (LULUC). Land use of HPPs implies land transformation (flood area and implantation) and occupation (entire occupied area concerning the time of use). According to Dorber et al. [57], hydroelectric pumped storage and storage hydroelectric plants, which use dams to store water in reservoirs to allow flexible production of electricity, cause LULUC. In addition to filling the reservoir, also due infrastructure, including power lines and access roads.

It should also be noted in Fthenakis and Kim [58] that state the occupancy factor is indirectly proportional to the time that a given area is used to generate renewable energy, that is, the longer the time, the lower the occupancy factor. On the other hand, for the production of energy from non-renewable sources (biomass, coal, natural gas, etc.), the relationship is direct, that is, the longer the time, the greater the occupancy factor.

The analysis of land use, such as occupation or transformation of HPPs using LCA is necessary. Take the example of Balbina HPP in Brazil. According to Fearnside [53], this plant will produce more GHG than fossil fuels because the proportion of the reservoir size per unit of energy generated is very high. The example of the LCA of the Curuá-Una plant in Geller and Meneses [39] in the Amazon region can illustrate these conclusions. The result of the analysis in this regard was 1.33E-02 km2.y/MWh. For an efficiency of 92.89%, its projected production is 29,976,720 MWh in 100 years. By reducing the operating time to 50 years considering the same efficiency, the HPP would produce half of that energy, and consequently would increase its occupancy factor and its environmental damage.

5.5 Others important factors that influence the emissions of HPPs

The environmental impacts produced by reservoir and run-of-river plants were presented by Gagnon and Vate [59]. For reservoir plants, the average was 15 kg of CO2-eq/MWh and for run-of-river plants, the average was 2 kg of CO2-eq/MWh. As already mentioned, steel and concrete are the main contributors to these emissions, as they are consumed in greater quantities in reservoir plants. The authors detailed the factors that contribute to emissions in the construction phase and the study includes the following criteria: (i) run-of-river hydroelectric plants versus reservoir plants; (ii) materials used (earth/rock versus reinforced concrete); (iii) volume of dikes and dams, which may be site-specific; and (iv) total size of the project or group of plants [59].

An important issue to consider when comparing run-of-river and reservoir plants is the reliability of the electricity supply. LCA studies of HPPs present results pointing to better performance for run-of-river HPPs [33, 37, 46, 47]. However, according to Gagnon and Vate [59], reliability is only possible when the energy source (water) can be stored in a reservoir, being available for operation. We emphasize that production reliability must also be considered when comparing reservoir HPP with plants that have intermittent sources, such as wind and solar photovoltaic. In the absence of these resources (wind and sunlight) it will be necessary to complement them to meet the demand, which is usually through fossil resources, greatly increasing the environmental impact.

Another issue to be considered is the use of reservoirs for other purposes, such as irrigation, flow control, flood control, storage of drinking water and even fishing. Currently, large reservoirs are planned and built for these purposes, such as the Three Gorges HPP in China, which uses stored water to control the flow available during the dry season, to facilitate navigation and to irrigate [32, 47]. On the other hand, there are cases, such as the Sardar Sarovar dam, Gurajat in India, where the main purpose of its construction was the irrigation of arable areas and the storage of water for domestic and industrial consumption, while the generation of hydroelectric energy was considered a side benefit, although more recently it has increased its importance [60]. The study of LCA in these cases becomes difficult because the importance of the reservoir as an energy producer is not fully known [35].

The type of technology used its manufacturing location and the distance of travel for the implantation in the plant is a significant factor for the environmental impacts [31]. Transporting this equipment from origin to destination consumes fossil fuel, leading to a significant increase in emissions. Like the Curuá-Una HPP, in Brazil, in which most of the equipment was manufactured far from the implantation site and it was often necessary to transport turbines and generators by river and road [39].

5.6 LCA of HPPs uncertainties and future challenges

Some uncertainty factors in LCA of HPPs were listed in this study. In general, it can be attributed to the lack of a standard for applying the methodology. However, the complexity and diversity of these systems makes this standardization difficult.

Initially, the use of a standard functional unit in the comparison of the different plants is not enough to guarantee a correct analysis of the results. There is divergence in the results when we have variation in the limits or boundaries of the system. Is important know well which specificities of each study will be included in the inventory.

For example, the LCA methodology applied to plants that have a reservoir most of the time includes all phases of the life cycle, namely, construction, operation/maintenance and decommissioning. However, there are few reports of LCAs that include the measurement of methane and carbon dioxide emissions that occur during the filling of the reservoir or the flooded area during the operational phase. There are many factors that can influence these measurements, such as pre-existing vegetation, location and climate, size and depth of the reservoir, among others. It is noteworthy that these emissions can significantly influence the results [52].

According to Littlefield et al. [34] the uncertainty factors are greater in the production of energy from renewable sources and Turconi et al. [33] add that among these, biomass and hydroelectric technologies are the most divergent in the LCA results (Tables 1 and 3). Temperature variation is also an important factor, according to the authors [33, 34], and reservoirs in tropical regions may be subject to higher GHG emission due to high temperature, which accelerates the biomass decomposition process.

Considering the above, there is a need for more specific conduct for each case of HPP LCA. This constitutes a great challenge. As a contribution to facilitating this process, we include the following recommendations: (i) elaboration of a framework to assess uncertainties; (ii) definition of a standard to classify dams according to their size, geographic location and climate; (iii) a framework to conduct the collection of essential data for hydroelectric plants, defining the system boundary in the inventory phase; (iv) construction of a common vocabulary, a practical application guide, so that researchers from other areas can contribute to the application of the LCA of HPPs methodology, with the objective of reducing these uncertainty factors.

The conclusions lead us to enumerate some alternatives to reduce the environmental impacts in energy production from HPPs: (i) future research to quantify emissions from hydroelectric plants should be conducted to include their entire life cycle, from cradle to grave, considering direct and indirect emissions; (ii) using LCA to analyze emissions from large hydroelectric plants located in developing countries such as China, Brazil and India can bring elements to a better choice of energy production in very extensive territories; (iii) the integration between different technologies of renewable sources, in an optimized way, can be a good alternative to meet the growing demand considering the parameters of sustainability; (iv) choosing more modern technologies for infrastructure (turbines, dams, pipelines, etc.) can reduce emissions during the construction phase of hydroelectric plants.

We understand that the optimized integration of different technologies in energy production is a viable way to meet the demand and, in this context, the hydroelectric plant plays an important role. However, the challenges of meeting the economic, social, and environmental viability of these projects have caused many concerns, discussions, and controversies.

Some research considers hydropower to be a clean, renewable energy source, while others claim that its emissions can sometimes be even higher than those of fossil fuels. It is important that this topic is discussed in a context based on the results of peer-reviewed scientific studies, such a scientifically based discussion will allow the rigorous evaluation of the most viable forms of energy production that attend the principles of sustainability.

6. Conclusion

The chapter presents a review of the HPP’s LCA and compiles aspects that may influence these analyses, therefore the main contribution of this work is to highlight the different characteristics of the HPPs that impact the analysis of their indirect emissions.

Among the factors mentioned are the capacity and scale of projects, pointed larger systems have better environmental performance than smaller systems, due the ratio between the emission rate and the energy produced in MWh in the plant’s lifetime.

Regarding the type if is reservoir or run-of-river, there is a factor that must be considered. Although reservoir plants produce more emissions, authors cite the use of these reservoirs for other purposes (water flow control, fishing, etc.), bringing additional benefits.

Land use must be critically analyzed when dealing with HPPs that use dams, due to the construction of its infrastructure including power lines and access roads. Although, the longer time a certain area is used for generation of renewable energy the lower is the occupation factor.

It is notorious and research shows that HPPs produce less emissions per energy generated than technologies that use fossil fuels. However, to evaluate the results among only renewables sources, such as wind, solar and hydroelectric, it is necessary to consider the intermittence of sources, that is, the constant availability of the natural resource (wind, water, solar) to generate energy. And this factor corroborates to the better performance of HPP in some cases.

Regarding the phases analyzed by the HPP’s LCA, most studies state that the construction phase is the one that produces the most emissions in relation to renewable plants. But some studies also cite emissions from the flooded area and submerged vegetation as an important emission factor.

The results obtained by the research with LCA methodology exemplified here confirm that it can be widely used for the environmental analysis of electric energy production systems. However, the lack of standards for the preparation of inventories and for their analysis is pointed out by many authors as one of the difficulties for the use of LCA. The authors list in the challenges section some suggestions to minimize this problem.

In this context, it is considered that the more studies of the environmental impacts produced by the indirect emissions of HPS, using LCA, the more knowledge about the adequacy of the use of this methodology, the uncertainty factors and their restrictions will be consolidated, thus collaborating in the decision process involving HPPs.

Acknowledgments

The authors acknowledge the support of the Foundation for Research Support of the State of Pará (FAPESPA).