We use energy in our homes for heating, washing, cleaning, cooking, lighting, and powering our appliances for entertaining, socialising and interacting. Energy use is essential for our health, wellbeing, and comfort. However, domestic energy use is also responsible for almost a fifth of UK carbon emissions which have to come down to zero in the next 30 years. Energy use in homes is also the biggest driver of demand during the peak winter period which means it determines the amount, type, and cost of power generation capacity we need to have available to meet this peak. Currently, fossil fuels are often used to generate electricity to meet the extra demand for electricity during peak periods. If the UK is to reach net-zero emissions by 2050 in line with national laws and our international commitments, we must reduce energy use in homes and stop using natural gas. Installing heat pumps and insulating homes will cost many billions of pounds when scaled up across the UK . This will happen at the same time as more electric vehicles are being charged at home, and more homes are generating and storing their own energy. It's essential we understand how these complex and overlapping changes will affect the UK's energy system. To use recent vocabulary, we need a 'track and trace' system for energy use in our homes, enabling us to understand how, why, and when domestic activity is impacting energy demand and associated carbon emissions. The data from smart meters help us understand these changes but it isn't enough to understand the complex picture that will be emerging in the near future. To understand new technologies, policies and business models that are appearing all the time, we need a much higher resolution data resource. EDOL will provide this. The UK has led the world in access to high quality energy data and its analytics. The EDOL programme will build on the multi-billion investment in smart meters and their communication system and utilise emerging Internet of Things (IoT) and AI innovations to help us understand how and why we are using energy in the home. EDOL will develop a range of innovative methods for monitoring not only the energy consumed by different appliances, but also the different energy-using activities that make up daily life at home. EDOL will then build a 2,000-home Observatory that is representative of the GB population of people and buildings. EDOL will monitor the energy used by occupants, their appliances and their behaviours. All data collected from people's homes will be done so with the occupants' informed consent. Data will then be anonymised and provided to researchers in a secure data portal for analysis to help understand and build better models of energy demand in our homes. In sub-samples of homes with uncertain, unexplained, or novel forms of energy demand (like smart charging of electric vehicles), we will undertake targeted 'forensic' monitoring to enrich our understanding. Alongside the Observatory, we will recruit homes where new technologies, business models, policies and monitoring approaches can be trialled. These EDOL field Laboratories will allow us to answer novel questions: e.g., 'How flexible is time when people charge their electric vehicles?', or 'Does installing a heat pump have unintended consequences such as increased tumble drying of cloths due to lower radiator temperatures?'. We will build several field Laboratories and support other academics, government and business who may want to commission their own. Having the Observatory alongside these Laboratories means that we can tell how big the effects are by comparing the treatment group (Laboratory) with our control group (Observatory). We will work closely with government to maximise the benefit of our data and analysis to support progress towards a net-zero energy system. We will also support other researchers by facilitating secure access to the data we collect and the methods we have developed.

This Knowledge Exchange fellowship has two strands i. The Engagement focus aims to increase government use of academic work, by increased engagement between BIS and NIESR through a) a series of seminars for policy people, b) review of processes for government research and data management, and c) improved communication of the policy perspective. ii. The Research focus aims to undertake analysis of firm level data and explore the potential to extend the findings in the literature in order a) to gain greater impact on policy development from academic knowledge, and b) identify and develop potential for further policy relevant research from these areas of NIESR expertise. The Engagement focus of the project contains different elements: a. Seminars. NIESR has agreed to arrange a series of 4 seminars on BIS policy topics over the 12 months of the fellowship. These will involve experts from the academic community, including ESRC centres such as SKOPE, and will be open to government officials from BIS and other departments. The topics are likely to include areas such as macro-economic projections, migration and the labour market, and sources of productivity gain. b. Research and data processes. The opportunity of the Fellowship will be used to improve academic links with government processes for research and data where possible. The aim is to develop and provide greater feedback to academic bidders on the features of weaker bids, and how to strengthen them. c. Policy context. As well as policy relevant seminars, other steps will be taken to ensure that BIS priorities are conveyed to the academic community. The approach will be to arrange a workshop for academics, hosted by NIESR, and invite policy leads from government to provide feedback on what has worked for them. The Research focus will use firm-level micro-data to explore the relation between the dynamic economy (firms closing and opening, growing and contracting, improving and declining) and productivity, the labour market, and one or more of skills, innovation, high growth firms, sectors and clusters. The approach is to explore the potential of these data for greater insight and impact, rather than direct data analysis - in recognition of the challenges presented by the patchy documentation, and inconsistent data records. The starting point is the finding that the great majority of productivity gain arises from 'external restructuring' of firms or plants (80-90% of Total Factor Productivity according to Disney et al (2003), and greater according to Harris and Moffat (2012). At first sight this seems surprising - it suggests that little productivity gain occurs within existing firms or plants. The implication is sometimes drawn that economic churn amongst firms is the route for growth rather than improvement within firms. The picture could in fact be different. The empirical literature appears to focus mainly on churn among plants rather than firms and there is little detail on how much of the gain occurs between plants but within firms, and how much between firms. This is the first question that will be explored. The second stage will consider how this decomposition varies over time, under different macro-economic circumstances. The literature suggests there is some but not much variation. One would expect the components of change to differ at times of growth from times of contraction or recession. The third stage will assess the feasibility of separating the decomposition by firm size, as churn is likely to operate rather differently for small firms than for middle or larger ones. Policy on investment in areas such as skills or innovation will be informed and influenced by the findings. The fourth stage will explore the potential for future work in one or more policy areas - skills and training (building on work by Haskel et al 2003, Dearden et al 2005, and Galindo-Rueda et al 2005), in innovation, or employment.

While employment in Britain is at record levels, there is widespread concern many jobs are not of sufficient quality to maintain a healthy and thriving society. Growing public concern culminated in the government commissioning the 'Good work: The Taylor Review of Modern Working Practices' in 2017. A key recommendation of the Taylor Review was that the government should adopt a multidimensional definition of 'good work', among other recommendations. Building upon decades of academic research demonstrating their relationship with job-related wellbeing, the Taylor Review identified six dimensions as central to 'good work' (DBEIS 2017: 12) (wages, employment quality, education and training, working conditions, work-life balance, and consultative participation/collective representation). The overall objective of this SDAI project is to explore an occupational approach to mapping, understanding, and improving the quality of working life by applying insights from sociological theories of stratification which suggest that the capacity to achieve high job-related wellbeing is to a large extent determined by occupation-field of work. However, this issue has been scarcely researched. The extent to which job quality and job-related wellbeing are structured across the occupational structure are critical issues to understanding and developing pathways to improving the quality of working life, for instance, through occupational mobility or workplace practices that might moderate the effect of occupational environment. We propose creating a new Classification of Occupational Quality (COQ) for this purpose. This is because existing occupational classifications (such as the NS-SEC occupational class schema used by the ONS) were not intended to map job quality defined in a multidimensional way, and as such tended to focus on only a single job quality dimension. A more appropriate tool for the current academic and policy context is necessary. Moreover, the sparse existing research findings suggest that dimensions of job quality and measures of job-related wellbeing do not neatly map onto occupational classes in any case. The specific research questions motivating this proposal are: 1. What is the structure of 'occupational quality'? 2. How does occupational quality influence individuals' subjective wellbeing over the life course? 3. Is mobility across occupational quality structure an effective means of improving the quality of working life? 4. To what extent does the workplace moderate the effect of occupational quality on job quality and wellbeing? Using existing ESRC data, we will answer these questions through writing-up and submitting the results to four world-class academic journals. Emerging findings will be shared at, and feedback will be gathered from a range of national and international conferences, as well as specialist workshops with targeted academic experts to ensure maximum academic impact. A distinctive part of our SDAI project is its impact strategy beyond academia. With the support of the Dept BEIS (the department responsible for implementing the government's job quality strategy) and the CIPD (the professional body of the HR profession who have been a leading voice in the job quality debate), we will channel our findings to policy and practitioner audiences (see Letter of Support). This includes a series of policy and practitioner workshops, as well as plain English briefings of our research outputs, to be hosted on the project website (www.qualityofworkinglife.org). We will also enlist a design agency to prepare searchable and graphical presentations of occupational quality data we will produce from ESRC data. The project website will also host short video factuals which we will produce, summarising each paper. Collectively, these strategies will ensure maximum impact at a time when the issue of job quality has never been so pressing as well as maximising return on existing ESRC investments.

The tax and benefit system and education finance regime alter the value of human capital investments (e.g. education) by treating up-front costs, forgone earnings and future returns differently, and by insuring against earnings risk. In so doing, they affect the incentive to invest in human capital compared with not investing, and compared with other kinds of investment, such as financial investments. These are potentially important distortions that policymaking needs to take into account. The overall aim of this project is to use Understanding Society and British Household Panel Survey data, together with lifecycle modelling techniques, to provide new evidence on how the returns to human capital investments are distorted by actual and hypothetically-reformed UK tax and benefit systems and education finance regimes (including those in Scotland, Wales and Northern Ireland). To that end, we will address the following research questions: - How are investments in human capital taxed? - How do tax rates vary across different population subgroups and stages of life? - How effectively do taxes, benefits and the education finance regime insure against uncertainty in returns? - How do these features vary under different policy environments? - How do behavioural responses affect these results? Although some work has been done in this area, there remains much about the way that the tax and benefit system and education finance regime treat human capital investments that we do not know. Existing work has the common disadvantage that it is all for example individuals in idealised settings. This means there is limited consideration of variation across individuals, no attention paid to uncertainty, rudimentary modelling of taxes and benefits and no allowance for the behavioural effect of policies. These are important limitations, which our proposed approach will address. We will consider two types of human capital investment - education and labour market experience - and use two measures of how investments in human capital are taxed - an effective tax rate (ETR) and a participation tax rate (PTR). The ETR measures how much the internal rate of return to investment is affected by taxes, benefits and subsidies. This is the measure that has been used previously to describe, e.g. how the tax system treats the return to saving held in different assets. The PTR describes what proportion of the return to investment is lost due to higher taxes and lower benefits, possibly offset by subsidies. This has been widely used in the literature on financial work incentives. Our analysis will be for the UK and will be based on the Understanding Society and British Household Panel Survey (BHPS) datasets, supplemented with published data on educational spending per pupil from Government departments and agencies (e.g. the Education Funding Agency) and previous IFS work, and information about education-related expenses from the Student Income and Expenditure Survey. In the first stage of the project, we will use these data to produce descriptive analysis of employment, earnings and human capital investment costs. In the second stage, we will use the data and the results of the first stage to estimate how investments in human capital are taxed. This will be based on a lifecycle model of education, employment and saving choices that will allow us to calculate the ETRs and PTRs described above. This research represents a significant advance over the current literature, providing accurate and detailed estimates of the tax rates on investment in human capital and developing a methodology that can be applied in other settings. We also anticipate having significant impact on policymakers, improving understanding over how the tax and benefit system and education finance regime affect incentives to accumulate human capital, and affecting future policy decisions. We will exploit project partners in BIS and HMRC to ensure these impacts are realised.

In 2015, the Governor of the Bank of England issued a stark warning that both the impacts of climate change and those of climate policies could have pronounced effects on the UK's financial and insurance industries. At the core of climate-related impacts, there is a dilemma: while climate policy seeks to avoid long-term physical damages from climate change, it may also negatively affect financial markets as the valuation of fossil fuel-related financial assets falls. Such assets could become effectively 'stranded' by the transition to environmental sustainability. The financial risks arising from the transition and the stranding of fossil-fuel assets have been termed 'transition risks'. The adoption of climate laws and policies, including the 2008 Climate Change Act in the UK, has supported the development and uptake of low-carbon technology, significantly reducing the demand for fossil fuels worldwide. This change in demand, together with the expectation of more stringent climate policies in the future, is rapidly changing the market outlook for fossil fuel-related industries and associated physical and financial assets. Companies could invest in oil wells, refineries or tankers that, in a scenario of low demand, may fail to generate the expected return and thus become stranded. If the risk of asset devaluation is underestimated by investors, a climate bubble - which may already exist - could grow significantly, the bursting of which could lead to panic selling and a propagation of losses across the financial network, potentially triggering financial instability as severe or worse than that experienced worldwide in 2007. This project aims to develop a robust characterisation, quantification and communication of climate-related transition risks, thus addressing a key objective of the UK Climate Resilience programme. To achieve this, we will improve and apply a set of software tools and a consultative analytical procedure to assess the risks to the UK's financial and economic stability of a rapid transition to a low-carbon economy, and its impact on the real economy, jobs and income. We have recently developed a new computer model of the energy-economy-environment system uniquely well suited to this problem. The potential global loss in value of fossil-fuel assets we estimated to be $1-4tn, and its impact on the macroeconomy potentially twice as large. Our model represents the first of a new generation designed to assess the impacts of detailed climate policy packages on global and national economies. The model simulates the uptake of key low-carbon technologies in the most emissions-intensive sectors (power generation, road transport, household heating, industry and land-use). To address the issue of financial contagion, triggered by fossil-fuel asset devaluation, we will map out the network of ownership of fossil-fuel assets to create a form of "fossil-fuel financial geography". Focusing on the largest privately and state-owned companies and covering most of the global value at risk, we will gather data on the distributions of both their physical fossil-fuel assets and the main investors in their financial assets, thus mapping the principal linkages between fossil-fuel assets and financial actors. Using this map, together with our modelling toolkit, we will explore the vulnerability and resilience of the UK's economy and financial sector, and investigate the magnitude and distribution of potential impacts. Our ultimate goal is to identify and explore risks and assess strategies and responses that could reduce climate-related transition risks and improve the UK's economic resilience.