1 Introduction
2 Approach
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Autonomy. Defined as being in control of oneself and one’s resources, autonomy is negatively impacted by for example, forced labour or slavery.
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Safety, security and tranquillity. It is a combination of freedom from threats to human health and property. It also includes aspects related to the beneficial impact of employment (which goes beyond receiving a salary but also include issues such as satisfaction).
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Equality. It represents the level of disparity among countries and or regions. Equality is for example negatively impacted by increasing disparity in income distribution.
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Participation and influence. Defined as ‘the act of taking part or sharing in something and affecting the course of event’ (Farlex Inc 2012). It includes the level of participation in decision-making processes.
3 Results—general considerations
3.1 Functional unit
3.2 System boundaries
3.3 Definition of foreground and background processes
3.4 The baseline
3.5 Level of analysis
4 Results
4.1 Measuring social well-being: selection of indicators
Indicator (unit) | Definition | Type | Desired direction for sustainability |
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Safety, security and tranquillity | |||
Knowledge-intensive jobs (h) | High-skilled employment. Includes workers as managers, professionals, technicians and associate professionals for which education is required | Quantitative | Positive |
Total employment (h) | Share of the labour force—the total part of society that is available for work—that is working | Quantitative | Positive |
Possibility of misuse | Potential use of the technology that causes harm to people or society. The vulnerability of the novel technology to be used in hazardous ways such as sabotage or terrorism. | Qualitative | Negative |
Risk perception | Observation of hazard by the general public. The perception of risk can cause instability because of decreasing overall feeling of safety in a society | Qualitative | Negative |
Autonomy | |||
Child labour (h) | Work that deprives children of their childhood, their potential and their dignity, and that is harmful to physical and mental development (ILO 2013). It concerns hazardous work done by children and other severe forms of child labour | Quantitative | Negative |
Forced labour (h) | Work or service which is exacted from any person under the menace of any penalty and for which the said person has not offered himself voluntarily | Quantitative | Negative |
Equality | |||
Income inequalities (GINI coefficient) | Structural disparities between salary levels, representing a gap between rich and poor. The indicator regards the degree to which global income inequalities are affected by the introduction of the novel technology | Quantitative | Negative |
Regional inequalities (€) | Disparities between GDP levels around the world, comparing the GDP levels of developing countries with those of developed countries | Quantitative | Negative |
Participation and influence | |||
Trust in risk information | Confidence of being informed in case of hazard. The indicator regards the degree to which the general public feels confidence that they will be informed in case of hazard. | Qualitative | Positive |
Stakeholder involvement | Active participation of interested parties within decision-making processes. The indicator regards the degree to which the interested parties are involved within decision-making processes concerning the novel technology | Qualitative | Positive |
Long-term control functions | Governance or technical instruments such as regulating authorities or redundant systems that ensure long-term control will. The indicator regards the degree to which people trust that the technology is adequately controlled | Qualitative | Positive |
4.2 Operationalizing the indicators: performance assessment
4.2.1 Quantitative indicators
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Knowledge-intensive employment. This indicator uses information on direct and indirect high-skilled labour requirements for the studied technology, which can be modelled for instance by economic input-output models. Data on requirements of high-skilled labour is available in THEMIS and is used to represent knowledge-intensive employment (Simas et al. 2015). This indicator regards the relative increase or decrease in high-skilled employment caused by the introduction of the novel technology in comparison to the high-skilled employment caused by the introduction of the reference technology. The indicator is used as a proxy for the effect on the knowledge-intensive economy which contributes to productivity and economic growth (OECD 2013).
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Total employment. It uses data on direct and indirect labour requirements, which can be obtained from economic input-output models. This information is similarly available as knowledge-intensive employment. This indicator regards the relative increase or decrease of total employment caused by the introduction of the novel technology in comparison to the total employment caused by the introduction of the reference technology. The indicator is used as a proxy for the value of employment in society (see Ciroth et al. 2014).
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Child labour. This indicator requires information on whether there is child labour involved for the studied technology across its life chain, including the number of children involved per economic sector per country. This information was not directly available in THEMIS so data on child labour from the U.S. Department of Labor’s List of Goods Produced by Child Labor (U.S. DOL 2012) as well as data on the amount of persons involved in child labour per sector per region from the ILO (2010; 2012) was linked to information on total employment which could be extracted from THEMIS (Simas et al. 2014). The data on the number of persons working in child labour per sector is multiplied by an assumed average of 2020 working hours per year. This gives the total number of child labour working hours per region per sector. The share of child labour hours for aggregate sectors can then be calculated by dividing the total number of child labour hours per sector per country by the total number of working hours (total employment) per sector per country. The total number of child labour hours caused by the introduction of the novel technology can be calculated by multiplying the total employment caused by the introduction of the novel technology with the share of child labour hours per country per sector. A detailed discussion of the methodology as well as of the benefits and drawback of this approach can be found in Simas et al. (2014).
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Forced labour. Data is required on whether there is forced labour involved for the studied technology in the specific country and the number of persons involved per sector per country. Following a similar procedure to the one use for child labour, data of forced labour can be extracted from the U.S. Department of Labor’s List of Goods Produced by Forced Labor (U.S. DOL 2012) in combination with the amount of persons involved in forced labour per sector per region from the ILO (2012, 2010). The data on the number of persons working in forced labour per sector is multiplied by an assumed average of 2020 working hours per year. This gives the total number of forced labour working hours per region per sector. The share of forced labour hours for aggregate sectors can then be calculated by dividing the total number of forced labour hours per sector per country with the total number of working hours (total employment due to the implementation of the technology as modelled in THEMIS) per sector per country. The total number of forced labour hours caused by the introduction of the technology can then be calculated by multiplying the total employment caused by the introduction of the novel technology by the share of forced labour hours per country per sector. See Simas et al. (2014) for full details.
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Income inequalities. The indicator is measured as the change in the global income Gini as a result of the introduction of the novel technology. The baseline for the assessment is the global income Gini when only the reference technology is implemented. The Gini coefficient is a standard measure of income inequality (OECD 2011) defined as the relationship of cumulative shares of the employees (in %) arranged according to income levels, to the cumulative share of the total income (in %) received by that share (Eurostat 2013). The index is modelled with THEMIS which is calculated following a standard procedure that is described in Ciroth et al. (2014). A value of zero in the Gini coefficient expresses perfect equality (e.g. where everyone has an exactly equal income). A Gini coefficient of one expresses maximal inequality among values.
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Regional inequalities. This indicator represents the effect of introducing the novel technology to the economic disparities between world regions. For this indicator the effect is calculated for OECD countries and for non-OECD countries, both expressed as ∆GDP. In a second step, the difference between these ∆GDP values is calculated. If prosperity increases in developed countries, but decreases in developing countries, this can be interpreted as an increase in global inequality. Regionalized GDP data is extracted from THEMIS.
4.2.2 Qualitative indicators
5 Results
5.1 Aggregation
Name | Unit per capita | Per capita global normalization |
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Total employment | Hours | 8.98E + 02 |
Knowledge-intensive jobs | Hours | 1.95E + 02 |
Regional inequalities (GDP) | OECD GDP (€) - non-OECD GDP (€) | −2.19E + 03 |
Income inequalities (GINI) | N/A | 9.13E − 11 |
Children in hazardous labour | Hours | 1.96E + 01 |
Forced labour | Hours | 2.22E + 00 |
5.2 Case study example: electricity power sector with carbon capture and storage
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The functional unit is 1 kWh electricity (kWhe) delivered to the grid.
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The penetration of CCS will largely be regulated and is modelled against the backdrop of a carbon tax. This tax is taken from the IEA Energy Technology Perspectives scenarios for 2030 (IEA 2013). With the carbon tax applied, the cost of a unit of electricity is higher for the non-CCS case than the CCS case.
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The IEA Blue Map scenario of electricity from coal production for CCS is used as basis to establish potential market penetration. The reference case assumes all electricity from coal is produced without CCS, and the prospective case assumes all electricity from coal is produced with CCS (as per Blue Map)3.
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The cost data were taken from Matuszewski et al. (2012). The costs are expressed per functional unit, in the report given as the costs (dollars) per kWh for the first year of operation. The total costs provided in the report were used as an allocation key between components, and hence sectors in the input-output model. Due to this calculation method, only the costs per functional unit were converted to Euro.
5.2.1 Quantitative indicators
Indicator | Absolute difference (prospective to reference) | Observed trend | Desired trend |
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Total employment | 5.1 + E05 h | Increase | Increase |
Knowledge-intensive jobs | 9.2E + 04 h | Increase | Increase |
Child labour | 4.0E + 03 h | Increase | Decrease |
Forced labour | 3.8E + 02 h | Increase | Decrease |
Income inequality (GINI) | 1.0E − 08 | Decrease | Decrease |
Global inequality | −1.3E + 07 Euro1
| Decrease | Decrease |
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Total employment. Introducing CCS in the EU electricity sector has positive repercussions for the employment opportunities; with more work per unit output of electricity. The total working hours increase between the prospective and reference scenario due to the increased economic activity. The total working hours per functional unit increase by 73 % (71 % in the EU and 81 % in non-EU) which indicates that the impact of CCS in coal power plants in Europe have a slighter larger (positive) impact outside Europe. This is most likely due to the increase on material raw products (e.g. coal) that are needed (per unit of output) as a consequence of the reduction on efficiency of the power plant induced by CCS.
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Knowledge-intensive jobs. There is a higher increase in high-skill working hours than in the medium- and low-skill working hours per functional unit in the prospective system than in the reference system. Results from the model also show the shares of working hours, split into skills levels and sectors (Appendix 2). The introduction of CCS only has a minor effect on the structures, both in terms of skill level and sectors. As most differences in the structures are on a decimal percentage level, it is not possible to refer to structural changes. The increase in the high-skilled working hours is larger in non-EU- than in EU member states.
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Child labour. The impact of CCS on this indicator for the whole economy, though negative, is very minor (less than 0.001 %). However, in terms of hours per functional unit, the implementation of CCS results in a 70 % increase. Results of the model also allow examining the contribution of different sectors to child labour hazardous activities per functional unit. They show that fossil fuel exploration (coal mining, oil and gas exploration) has the largest contribution to the number of child labour per f.u. (see Appendix 3).
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Forced labour. The results show similar trends to those shown by the indicator on child labour, that is, an insignificant effect of CCS in the forced labour of the total economy. In terms of forced labour per functional unit, there is an increase of about 65 % as a consequence of CCS implementation. Appendix 4 shows the contribution of different sectors to the force labour (per functional unit). For both scenarios, the largest share is due to fossil fuel exploration. Forced labour within the EU accounts for about 50 % of the forced labour per f.u., in the reference scenario and a slightly lower share (47 %) in the prospective scenario.
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Income inequality. The model results indicate that implementation of CCS leads to a very small decrease in the GINI index. Furthermore, the global GINI points out that there is significant inequality worldwide. Results of the model allow exploring the GINI by region which indicates large differences in income inequality across regions, with the EU showing the lowest index and India the highest.
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Global inequalities. In terms of GDP per f.u., the results indicate a relative increase of about 80 % in OECD countries and a relative decrease of about 20 % in non-OECD countries. Nonetheless, in the macro-level studies this results in a (very small) decrease in the differences between OECD and non-OECD countries (note that non-OECD countries have a larger GDP than OECD countries). The impact of deploying CCS in the EU electricity sector can be considered as insignificant (<0.0001 %) when assessing macro-level effects.
5.2.2 Qualitative indicators
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Risk perception. Risk perception of CCS has, for instance, been examined by Wallquist et al. (2010) using a representative survey in Switzerland (n = 654). The authors indicate that predictors of risk perception are: socio-economic concerns, i.e. a perceived unsustainable character of CCS by the public (CCS as an end-of-pipe solution; CCS competing with renewable energy technologies; rebound effect), and concerns about leakage and the perception of pressurization in the geological reservoir. Their research also indicated that knowledge about CO2, storage mechanisms and the awareness of climate change decreases risk perception. Schakel et al. (2007) interviewed stakeholders across Europe (n = 512). The results indicate that those issues which are identified as being with the highest risk are: additional fossil fuel use because of the energy penalty, human health and safety from onshore CO2 storage and environmental damage from both onshore and offshore CO2 storage. The lowest levels of perceived risk are associated with accidents arising from inclusion of CO2 capture at power stations and human health and safety risks from offshore CO2 storage site leakage. Literature indicates that a moderate increase in risk perception can be expected when applying the prospective technology. The indicator therefore will be flagged in the final results as “Moderate concerns”.
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Possibility of Misuse. This indictor requires exploring potential economic consequences and consequences for safety risks. However, as it is not the intention at this stage to perform a full risk analysis, the focus is on identifying whether the implementation of CCS would increase the (infrastructure) vulnerability of the power plants (including up- and downstream). Vulnerability is examined in terms of likelihood of an attack. In this analysis it is argued that the implementation of CCS will not change the vulnerability of coal mining or coal transport, as CCS does not change the extraction or transport methods used. However, implementing a CO2 capture unit in the power plant changes the plant (from a technical point of view). Instead of sole combustion processes, also chemical processes are included, and therefore its vulnerability could increase as chemical plants have a higher risk of sabotage. The two new steps in the chain are CO2 transport and CO2 storage. In the scenarios CO2 is transported in pipelines in dense phase at large pressure (>80 bar), which is analogous to the transport of oil and liquefied natural gas. Oil and gas pipelines could be targets for terrorists, due to the direct large effects for the owners of the pipelines in the form of lost revenues and finding and repairing the leak. Besides, pipelines are relatively easy targets, since they are stretched out over large distances and therefore cannot entirely be protected and monitored. Uncertainties for CO2 storage are related more to safety and leaking risks than to the possibility of misuse. CO2 transport and CO2 storage have been further examined regarding the likelihood of loss. The method uses expert judgement to evaluate different aspects (see Appendix 5). Note that for this exploratory analysis, a group consultation with 8 experts was conducted. These experts have knowledge on energy systems and CCS but not specific knowledge on vulnerability assessment. Preliminary results flag CO2 transport with a moderate vulnerability and CO2 storage with a low vulnerability. Summarizing, the deployment of CCS in the chain will not change the vulnerability of coal mining and coal transport and will increase the vulnerability of the power plant (to level similar to those found for chemical plants). The two new steps (CO2 transport and CO2 storage) have a medium and lower vulnerability respectively. Taking into account the low economic value of CO2 and the fact that CO2 is considered to be transported in Europe, the whole chain is flagged with “low concerns” for increased vulnerability in the prospective scenario.
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Trust in risk information. Although it is not possible to forecast whether population will have trust in risk information when CCS is implemented in large-scale, it is possible to explore whether trust is considered a bottleneck in the current implementation of CCS projects. Reiner et al. (2012) examined shaping factors towards CCS in five European countries. Their results show a significant statistical relationship between perceptions about outcome risks of CCS technology and their trust in industry and national governments/politicians. The study also indicated a difference in the concerns on risk for different stages of the CCS chain. For CO2 storage, respondents who lived within 100 km of the storage site tended to be more concerned about risks than those who lived further away. For the capture site, the relationship was found to be more complex, with those who live quite near to the capture site were more positive towards the local project than those who live farther away. The authors expected local economic benefits from the project may explain this more positive attitude. Riesch et al. (2013) conducted a European study on public perception of CCS in Poland and Spain. Their findings point out a positive correlation between trust and perceived justice in the planning process and more favourable views on the CCS developments. Terwel et al. (2012) examined how the local public perceived a proposed CCS demonstration project in Barendrecht, the Netherlands. The survey was administered to a large sample of the Barendrecht population (n = 811) shortly before it was decided to cancel the project due to public opposition. More than half of the respondents (55 %) stated that they did not trust those who would ultimately decide about the CCS plan and only 10 % of the respondents had “quite a lot” or “very much” trust. Dütschke (2011) compared the drivers of local public acceptance in two cases from Germany (Jänschwalde and Ketzin). In the first case, the CCS demonstration project was stopped due to strong public opposition, the second case (Ketzin) has successfully been implemented. The author’s findings show that while in Ketzin public feels safe due to the minor quantities injected and the fact that the project would have to be stopped in case of leakages. The researchers from GFZ are trusted by the public and by community representatives. In Jänschwalde, however, the project developer was not trusted. A similar conclusion was drawn by a report on the lessons learned from the Jänschwalde project (European CCS demonstration project network, 2012). Based on the information available, trust in risk information appears as a potential bottleneck for CCS development. There is a large amount of research conducted at the moment on the drivers and potential strategies to manage public communication. The indicator is at the moment flagged as “Major concerns”.
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Stakeholder involvement. Ideally, stakeholder involvement should improve the quality of the decisions and their legitimacy among those involved and affected (Lippin Malone et al. 2009). A survey (n = 811) to a large sample of the population in Barendrecht (Terwel et al. 2012) indicated that most residents perceived the decision-making process as unfair. They further felt that project developer and the national government had too much influence in the decision-making process and that the people of Barendrecht had too little influence. Reiner et al. (2012) show that respondents who agreed that the current planning process gives sufficient voice to local concerns and that their local community was treated fairly in the past were more likely to be positive towards the local CCS projects. Similar conclusions were drawn by Dütschke (Dütschke 2011). In this study it was found that local history played a role in the level of acceptance (or opposition) towards a CCS demonstration project. The study also concluded that, if a society wants to include CCS as a part of its energy strategy, this also needs to be supported by several (local) stakeholders in order to convince people on a local level that it is worthwhile to take the risk of living above / near a storage site. Similar conclusions are drawn by several studies including the guidelines published by the World Resource Institute (Forbes et al. 2010). Based on the information available, stakeholder involvement appears as a key element for deploying CCS projects. Note that this indicator and the indicator on trust in risk information are interlinked (low level of trust will most likely be due to a perceived low level of stakeholder involvement). The indicator is at the moment flagged as “Major concerns”.
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Long-term control functions. Under this indicator the presence of governance or technical instruments that ensure long-term control will are included. In the case of coal power plants with and without CCS, the time frames at which storage occurs have been pointed as a main point of concern. Steenhouse et al. (2005) identified two independent timeframes that regulators would have to deal with for CCS. The first one relates to the ability of the storage to retain the total amount of CO2 injected, i.e. several hundred years. The second timeframe refers to the potential for CO2 stored underground to leak. The authors indicated that the time frame should take into consideration the potential impact for a long period of time (i.e. thousands of years). Given the longevity of the storage component of CCS projects, there is agreement that liability should be shifted from the private sector to the public (as represented by the state), but there is continuing debate as to when, by whom and how extensive this assumption of liability should occur (Bachu 2008). Zakkour and Haines (2007) identified key gaps in permitting regimes for the CCS chain and phase of operation. The authors indicate that permitting systems for capture and transport require little modification but major developments are needed for the subsurface element. Based on this information, the indicator is flagged as “moderate concerns”.
5.3 Aggregating the results
Indicator | Normalization factor | Weights set 1 | Weights set 2 |
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Total employment | 8.98E + 02 h/person | 0.167 | 0.197 |
Knowledge-intensive jobs | 1.95E + 02 h/person | 0.167 | 0.093 |
Child labour | 1.96E + 01 h/person | 0.167 | 0.293 |
Forced labour | 2.22E + 00 h/person | 0.167 | 0.166 |
Income inequality | 9.13E-11 GINI/person | 0.167 | 0.158 |
Regional inequality | −2.19E + 03 €/person | 0.167 | 0.093 |
Using weight factors 1 | Using weight factors 2 | |||
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Absolute difference (prospective to reference)—persons | Direction of change | Absolute difference (prospective to reference)—persons | Direction of change | |
Social well-being | 1100.7 | Desired | 627.1 | Desired |