Submitted:
10 December 2024
Posted:
11 December 2024
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Abstract
Keywords:
1. Introduction
1.1. Importance
1.2. Objective
2. Methods
- Step one is to determine the demand forecast, predicting the required megawatt capacity for each type of renewable energy technology across three different future energy transition scenarios. This allows me to determine the scale of mineral usage in each scenario.
- Step two is to determine the minerals needed for each technology type and calculate the required quantities based on the demand forecast.
- Step three is to collect data on the environmental impacts of each mineral during their mining stage. For the scope of this paper, only the mining stage will be focused on at this time.
2.1. Wind Models Overview
| Type of Generator | Type of Turbine | Application |
|---|---|---|
| Direct drive | High-Temperature Superconductors (HTS) | Offshore |
| Direct drive | Electrically Excited Synchronous Generator (EESG) | Onshore |
| Gearbox | Electrically Excited Synchronous Generator (EESG) | Onshore |
| Direct drive | Permanent Magnet Synchronous Generator (PMSG) | Onshore and offshore |
| Gearbox | Permanent Magnet Synchronous Generator (PMSG) | Onshore and offshore |
| Gearbox | Double-Fed Induction Generator (DFIG) | Onshore and offshore |
| Gearbox | Squirrel Cage Induction Generator (SCIG) – Without full converter | Onshore |
| Gearbox | Squirrel Cage Induction Generator (SCIG) – With full converter | Offshore |
| Gearbox | Wound Rotor Induction Generator (WRIG) | Onshore |

2.2. Solar Models Overview
- Cadmium telluride (CdTe)
- Copper indium gallium diselenide (CIGS)
- Amorphous silicon (a-Si)
2.3. General Scenario for Wind and Solar
- Power generation capacities: Future capacities not only depend on the current deployment rate but also depend on the policy agenda. Different carbon reduction goals will alter the requirements for generation capacity.
- Plant lifetime: Fluctuations in capacity, the rate of replacement, and maintenance all affect the quantities of materials needed.
- Sub-technology market shares: Each model has different levels of efficiency and its applicable areas. It is worth noting that all sub-technologies sum up to one, so an increase in one sub-technology must mean a decrease in the other. Costs, efficiency, and mineral intensities are the key differences among different subtypes.
- Material intensities: Generally, material intensities are likely to decrease as technology becomes more mature.
2.4. Wind Demand Scenarios
2.5. Solar-PV Demand Scenarios
2.6. Estimates of Annual Global Demand
3. Results
3.1. Solar


3.2. Wind


3.3. Environmental Impacts
4. Discussion
4.1. Implications
4.2. Uncertainties
4.2.1. Analyzing Scenarios, Forecast Factors, Market Share, and Technological Mineral Intensities
4.2.2. Environmental Factors: Consistency Limitations, Limited Data, LCA Variations, and Global Scope
5. Conclusions
References
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| General Scenario for Wind and Solar | Description | Goal | Source |
|---|---|---|---|
| Low Demand Scenario (LDS) | All scenarios in the ETP report are constructed using a combination of forecasting to reflect known trends in the near term and “backcasting” to develop plausible pathways to a desired long-term outcome. The scenarios should not be considered as predictions, but as analyses of the impacts and trade-offs of different technology choices and policy targets, thereby providing a quantitative approach to support decision making in the energy sector. The ETP scenarios are complementary to those explored in the International Energy Agency (IEA) World Energy Outlook (WEO). LDS is developed given the current technology available. | Pledged to limit temperature increase within 2.7°C | The Energy Technology Perspectives (ETP) 2017 report published by the IEA. |
| Medium Demand Scenario (MDS) | Same as LDS. | 50% chance of limiting average future temperature increases to 1.75°C. | The Energy Technology Perspectives (ETP) 2017 report published by the IEA. |
| Energy sector emissions reach net zero around 2060. | |||
| High Demand Scenario (HDS) | The scenario was set up based on multiple simulations and outputs from various computer models. It is a technical pathway, not a political prognosis. It refers to technically possible measures and options without taking into account societal barriers. Efficiency and renewable potentials need to be deployed even more quickly than in the 2.0°C Scenario (which was not included in this paper), and avoiding inefficient technologies and behaviors is an essential strategy for developing regions in this scenario. | Pledged to limit temperature increase within 1.5°C. | The Institute for Sustainable Futures of the University of Technology Sydney. |
| 100% renewable primary energy in 2050. |
| Minerals | Countries of Extraction Sites | Processing Stage | Data Source | CO2eq Emission (tCO2-eq per metric tons of minerals) |
|---|---|---|---|---|
| Iron and Steel | Average of major countries | Mining | IEA (2021) | 0.03 |
| Class 1 Nickel (sulfide) | Average of major countries | Mining | IEA (2021) | 5.3 |
| Lithium Carbonate | Average of major countries | Mining | IEA (2021) | 1.46 |
| Zinc | Average of major countries | Mining | IEA (2021) | 1.18 |
| Aluminium | Average of major countries | Mining | IEA (2021) | 4.9 |
| Cobalt | Average of major countries | Mining | IEA (2021) | 2.09 |
| Copper | Australia | Mining | Northey et al. 2013 | 3.21 |
| Copper | Canada | Mining | Northey et al. 2014 | 1.63 |
| Copper | Chile | Mining | Northey et al. 2015 | 2.4 |
| Copper | Finland | Mining | Northey et al. 2016 | 0.45 |
| Copper | Laos | Mining | Northey et al. 2017 | 2.84 |
| Copper | South Africa | Mining | Northey et al. 2018 | 8.51 |
| Copper | Turkey | Mining | Northey et al. 2019 | 1.07 |
| Copper | PNG | Mining | Northey et al. 2020 | 1.26 |
| Copper | USA | Mining | Northey et al. 2021 | 4.38 |
| Copper | Average of major countries | Mining | IEA (2021) | 3.2 |
| Neodymium Oxide | Average of major countries | Mining | IEA (2021) | 13.2 |
| Other Impacts | Countries | Activity Stage | LCA Studies | Literature Source | Freshwater Eutrophication (kg phosphate-equiv / kg of metal) | Freshwater Ecotoxicity (CTUeco/kg) | Water Use (m³/kg) | Acidification Potential (kg SO2-equiv / kg of metal) |
| Aluminium | Global | Production | Cradle to gate | Nunez and Jones, 2016 | 0.011 | 0.018 | 0.13 | |
| Aluminium | Global, except China | Production | Cradle to gate | Nunez and Jones, 2016 | 0.0053 | 0.01 | 0.09 | |
| Cobalt | Average of major countries | IEA (2021) | 0.0000318 | 0.52 | 0.057 | |||
| Copper | Average of major countries | IEA (2021) | 0.01 | 9.25 | 0.032 | |||
| Rare Earth Elements (REE) | Average of major countries | IEA (2021) | 0.0213 | 538 | 0.635 | |||
| Iron | Average of major countries | IEA (2021) | 0.0006 | |||||
| Nickel | Average of major countries | IEA (2021) | 0.014 | 17.52 | 0.053 | |||
| Lithium | Average of major countries | IEA (2021) | 0.0013 | 5310 | 0.773 | |||
| Silver (from couple production) | Papua New Guinea and Sweden | Refining | Cradle to gate | Farjana et al., 2019 | 1.58 | 330.61 | 5.45 | 6.79 |
| Silver (from combined production) | Papua New Guinea and Sweden | Refining | Cradle to gate | Farjana et al., 2020 | 0.05 | 54.03 | 0.36 | 3.98 |
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