Sector Model Research Articles

Towards an ecological transition in the construction sector through the production of new eco-efficient products

The economic model of the construction sector is becoming more circular by emphasizing sustainability at all stages from the design, the production phase, the installation, the use, the removal and finally its transport to landfill. This work analyses the influence of added polyurethane foam waste from refrigerated industry on the technical properties (bulk density, water vapor permeability, and reaction to fire performance). The environmental properties were also measured through a comparative Life Cycle Analysis (LCA) between the traditional gypsum ceiling tile and a new organic one. The re-use of polyurethane waste in ceiling tiles causes technical improvements. The most relevant improvements noticed are the decrease in the density (28%), reduction in thermal conductivity (27%) and A1 classification for fire reaction. The comparative (LCA) between both tiles shows that the new product that incorporates polyurethane waste has significant improvement in CO2 (14%) and lower use of energy during the manufacturing process (14%). The following impact categories were also reduced: 9% in the case of the acidification of the ground and water, 9% for eutrophication and 31% reduction for non-hazardous waste. This work intends to reduce the carbon footprint and increase the sustainability of polyurethane gypsum products.

СТРАТЕГІЧНІ ПЕРСПЕКТИВИ РОЗВИТКУ СУПУТНІХ ПРОДУКТІВ МЕТАЛУРГІЙНОЇ ГАЛУЗІ В КОНТЕКСТІ ЦИРКУЛЯРНОЇ ЕКОНОМІКИ

The article examines the development of global metallurgical production within the framework of a formed circular economy system. It demonstrates that the circular economy of the metallurgical industry is based on efficient resource utilization within enterprises and adherence to stability indicators. An innovative transformation process from a linear model of the metallurgical sector to a circular economy model (a closed-loop economy) has been identified. Strategies for secondary processing, restoration, and re-production aim to extend the service life of products. The circular economy of the metallurgical sector eliminates stages of disposal and the concept of "waste." Instead, this new model relies on digital tools to enhance energy efficiency, reduce material intensity, employ decarbonization systems, and utilize by-products. The concept of 3R + co-P (reduce, reuse, recycle, co-products) has been defined, encompassing actions such as reduction, reuse, recycling, and co-products. The coproducts level involves the use of by-products (sludge, dust, slag, emulsions, used oils, technological gases, chemical substances). The reduction level is responsible for enhancing production efficiency and reducing costs. The reuse level characterizes the durability of steel, allowing for multiple product reuses after their service life. Besides extending the product's lifespan, reuse helps avoid the need for transportation and steel remelting. By-products such as converter gas, coke oven gas, blast furnace gas, and water also belong to the category of reusable resources. Melting steel scrap from products after their service life constitutes the third level of the circular economy in steelmaking production. Due to the magnetic properties of steel, it can be easily and readily extracted from almost any waste stream. Scrap metal serves as a material resource for metallurgy, preserving the resource potential of the mining sector. The article reveals the efficiency of by-products in metallurgical production and underscores their necessity for fundamental strategic sectors of the national economy (construction, mechanical engineering, chemical industry).

ФІНАНСОВО-ІНВЕСТИЦІЙНІ МЕХАНІЗМИ ЗАБЕЗПЕЧЕННЯ СТАЛОГО ЕКОЛОГО-ЕКОНОМІЧНОГО ЗРОСТАННЯ УКРАЇНИ

Financing of ecological and economic growth involves the implementation of specialized mechanisms and approaches that take into account the special requirements of sustainable development and effective use of natural resources. At the same time, in the conditions of global challenges and significant internal disparities, the field of so-called environmental finance is characterized by a number of problems. The analysis revealed that the beginning of Russia`s war in Ukraine led to additional challenges and changes in the field of environmental financing, since the crisis situation and the determination of budget priorities significantly affect the distribution of financial resources and planned activities in this field. State budget expenditures on defense as of June 2023 amounted to 52.75%, and they will increase as soon as the possibility of additional financing of its needs appears. At the same time, the funds of macro-financial international aid are directed to the general fund of the budget, from where all costs are financed, and there is also an agreement with the EU that their funds go to humanitarian needs and are not spent on financing the spheres of security and defense. The analysis of the current situation, as well as full-scale military actions in Ukraine, leads to the conclusion about the need to intensify the financing of green initiatives and projects, as well as the development of appropriate mechanisms for tools. According to the formalization, the relevant financial mechanisms should be implemented at several levels, in particular, similarly, through the prism of two models: sectoral and territorial models. Ensuring the financing of ecological and economic growth is an important task for any state, as it contributes to the balanced development of the economy, the preservation of natural resources, and the improvement of the quality of life of citizens. A number of measures aimed at expanding investment support are proposed, namely, the development of a comprehensive strategy for ecological and economic development, the creation of an attractive and stable investment climate, the development of innovative, digital technologies and the implementation of green solutions should be priorities, the state can attract funds through the issue of "green bonds", development of partnerships between the public sector, private companies and international organizations.

Verification of the effectiveness of discrimination models for forecasting bankruptcy of enterprises

Purpose: An attempt to evaluate the effectiveness of financial analysis tools used to assess financial health and to forecast bankruptcy. Methodology: The study used 31 of the most popular discriminatory models for bankruptcy prediction. The effectiveness of early warning models has been evaluated on the basis of financial data of economic entities operating on the territory of the Republic of Poland. The sample of the enterprises has comprised a total of 172 entities – both bankrupt and operating in good financial condition, located in 16 provinces. The data period was 2011-2020. The companies have represented various sectors of activity. Data was obtained from the Emis.com website. Findings: Most of the models used have been highly effective in forecasting. However, none of the methods has been 100% effective. It has also been noted that the models estimated on the basis of a sample of enterprises from only one sector, the so-called sector models allocated to the evaluation of a specific industry, have not had a significantly higher percentage of correct diagnoses than universal models. In the last analyzed period, the three most effective methods are the so-called universal models that can be used in the evaluation of companies from various industries. Practical implications: The results of the audit can be used, among others, in the assessment of the going concern of enterprises by business managers, business analysts, investors, and above all, statutory auditors when auditing financial statements. Originality/value: The study provides an answer to the question of which models are characterized by high prognostic effectiveness. In addition, the obtained results allow to resolve the issue of the usefulness of models created in the 1990s. The large size of the research sample allows for the generalization of the results and their wider application than has been the case in the literature on the subject so far. Keywords: bankruptcy, financial condition, discriminatory models, early warning models. Category of the paper: Research paper.

ПРІОРИТЕТНІ НАПРЯМИ РОЗВИТКУ ГЛОБАЛЬНОГО ПІДПРИЄМНИЦТВА УКРАЇНИ В КОНТЕКСТІ СТРУКТУРНОЇ ТРАНСФОРМАЦІЇ ЦИФРОВОЇ ЕКОНОМІКИ

The modern development of global entrepreneurship in Ukraine necessitates modernisation, taking into account the transformation of the digital economy, the difficult situation in the country caused by the war, national peculiarities of socio-economic relations and changes in the global market. The article investigates that the modern interpretation of the concept of entrepreneurship is closely related to innovative development as the main component of obtaining effective performance results to meet economic and social needs and increase the level of competitiveness. It is found that the economic component of global entrepreneurship development is primarily associated with the growth and expansion of services in the field of information and communication technologies based on the digitalisation of the economy. The state of the world economy in the recent period is analysed, and the results of the functioning of the Ukrainian economy in recent years are assessed within the framework of two approaches that differ in the criteria used. It is noted that Ukraine is moving to the formation of a new sectoral model of the national economy, which will address the transformation of its industrial society into a post-industrial one in accordance with the goals of sustainable development in the post-war period. It is found that the digital transformation of business can take place in two ways – fast but risky and gradual but more stable. The destruction caused by the war should be used as an opportunity to restore, modernise and expand the use of/ access to communication and digital infrastructure. In the medium term, numerous measures can be introduced to ensure that firms in the digital economy can access finance and talent, as well as reduce bureaucratic procedures. In the longer term, Ukraine could focus on building a robust data infrastructure to measure the digital economy. It is substantiated that a firm’s digital strategy requires a change in the entire management of Ukraine’s global enterprise development in the context of the structural transformation of the digital economy, especially the business model. Small and medium-sized enterprises can use business models focused on organisational processes and customer value. A number of recommendations are proposed to promote the development of global entrepreneurship in Ukraine in the context of the structural transformation of the digital economy.

Effects of Productivity Increase on Turkish Agriculture

In this study, an economic modeling approach based on nonlinear mathematical programming has been used. Positive Mathematical Programming (PMP) is the basic approach of our work. With the help of the Turkish Agricultural Sector Model (TAGRIS), which was defined on the basis of 81 provinces, the possible effects of a general increase in agricultural production productivity were examined. With the increase in productivity, plant and animal production increases compared to the basic period projection. With the effect of the price decrease, producer welfare decreases and consumer welfare increases according to the basic period projection. The increase in total welfare is limited accordingly. The highest production increase on a product basis is realized in the wheat, lentil and oilseed product group.

The Livestock Economy of Pakistan: An Agricultural Sector Model Approach

The Pakistan Agricultural Sector Model (PASM) developed by Davies et al. (1991) was modified to enhance the livestock sub-sector. Nutrient-based rations replaced feedstuff-based rations and dry matter minimum and maximum constraints (stomach capacity) were added. Several initial simulations were undertaken to examine the structure of the modified model and its impact across the crop and livestock sub-sectors. These simulations included relaxing exogenous livestock numbers and selected crop hectarage constraints, and requiring that green forage be fed in the season grown. Most importantly, the results demonstrated that fodder hectarage will grow with livestock numbers to insure that sufficient green forage is available seasonally. Two other analyses were performed to demonstrate the need to specify linkages between the crop and livestock sub-sectors. An analysis of transforming the livestock sub-sector from traditional to feedlot-based technology demonstrated that the reduced numbers of non-milking cattle needed for a given output of meat would provide the potential for increased production of various crops and other livestock products. Also, expanded cotton and Irri rice exports, hypothesised to occur through trade liberalisation from the Uruguay Round of the GATT, highlighted other inter-relationships between the crop and livestock sub-sectors. Greater production of both livestock and other crops might accompany the expansion of cotton production but less livestock feed would be available with expanded exports of Irri rice.

Learning to identify semi-visible jets

We train a network to identify jets with fractional dark decay (semi-visible jets) using the pattern of their low-level jet constituents, and explore the nature of the information used by the network by mapping it to a space of jet substructure observables. Semi-visible jets arise from dark matter particles which decay into a mixture of dark sector (invisible) and Standard Model (visible) particles. Such objects are challenging to identify due to the complex nature of jets and the alignment of the momentum imbalance from the dark particles with the jet axis, but such jets do not yet benefit from the construction of dedicated theoretically-motivated jet substructure observables. A deep network operating on jet constituents is used as a probe of the available information and indicates that classification power not captured by current high-level observables arises primarily from low-pT jet constituents.

Decarbonizing the International Shipping and Aviation Sectors

The Paris Agreement requires a drastic reduction of global carbon emissions towards the net zero transition by mid-century, based on the large-scale transformation of the global energy system and major emitting sectors. Aviation and shipping emissions are not on a trajectory consistent with Paris goals, driven by rapid activity growth and the lack of commercial mitigation options, given the challenges for electrification of these sectors. Large-scale models used for mitigation analysis commonly do not capture the specificities and emission reduction options of international shipping and aviation, while bottom-up sectoral models do not represent their interlinkages with the entire system. Here, I use the global energy system model PROMETHEUS, enhanced with a detailed representation of the shipping and aviation sector, to explore transformation pathways for these sectors and their emission, activity, and energy mix impacts. The most promising alternative towards decarbonizing these sectors is the large-scale deployment of low-carbon fuels, including biofuels and synthetic clean fuels, accompanied by energy efficiency improvements. The analysis shows that ambitious climate policy would reduce the trade of fossil fuels and lower the activity and the mitigation effort of international shipping, indicating synergies between national climate action and international transport.

Comparison of large eddy simulations against measurements from the Lillgrund offshore wind farm

Abstract. Numerical simulation tools such as large eddy simulations (LESs) have been extensively used in recent years to simulate and analyze turbine–wake interactions within large wind farms. However, to ensure the reliability of the performance and accuracy of such numerical solvers, validation against field measurements is essential. To this end, a measurement campaign is carried out at the Lillgrund offshore wind farm to gather data for the validation of an in-house LES solver. Flow field data are collected from the farm using three long-range WindScanners, along with turbine performance and load measurements from individual turbines. Turbulent inflow conditions are reconstructed from an existing precursor database using a scaling-and-shifting approach in an optimization framework, proposed so that the generated inflow statistics match the measurements. Thus, five different simulation cases are setup, corresponding to five different inflow conditions at the Lillgrund wind farm. Operation of the 48 Siemens 2.3 MW turbines from the Lillgrund wind farm is parameterized in the flow domain using an aeroelastic actuator sector model (AASM). Time-series turbine performance metrics from the simulated cases are compared against field measurements to evaluate the accuracy of the optimization framework, turbine model, and flow solver. In general, results from the numerical solver exhibited a good comparison in terms of the trends in power production, turbine loading, and wake recovery. For four out of the five simulated cases, the total wind farm power error was found to be below 5 %. However, when comparing individual turbine power production, statistical significant errors were observed for 16 % to 84 % of the turbines across the simulated cases, with larger errors being associated with wind directions resulting in configurations with aligned turbines. While the compared flapwise loads in general show a reasonable agreement, errors greater than 100 % were also present in some cases. Larger errors in the wake recovery in the far wake region behind the lidar installed turbines were also observed. An analysis of the observed errors reveals the need for an improved controller implementation, improvement in representing meso-scale effects, and possibly a finer simulation grid for capturing the smaller scales of wake turbulence.

Tourist choice, competitive tourism markets and the effect of a tourist tax on producers revenues

We propose a model for the tourism sector assuming basically two markets, one for tourist services and the other for accommodation. These sub-markets are considered as separate but interrelated. The nature of the feedback is determined by a vertical complementarity between tourist services and lodging. We obtain the optimal solution of the tourist choice problem, the primary demand for tourist services and the derived demand for overnight stays. Then, we focus on the equilibrium outcomes assuming perfectly competitive tourism markets. We do not address the externalities caused by tourism activities. Consequently, we move away from efficiency by introducing a tax on overnight stays and inspecting the consequences for the competitiveness and for producers’ revenues in each market. The answer key elements are, apart from reservation prices, the direct and cross-price elasticities of demand for tourist services. The study of structural parameters extends and completes our analysis of tourism.

Post-war restructuring of the agricultural sector of Ukraine: elimination of distortions to achieve stability and well-being

The relevance of the study is due to the formation of the rent-seeking model of the agricultural sector, the dominance of the raw material structure of export and the related socio-economic problems that have escalated to the scale of national security in the context of Russia’s war against Ukraine. The purpose of the research is to identify cause-and-effect relationships of structural distortions of the agricultural sector and justify the state regulatory policy aimed at strengthening its sustainability. The methods of empirical observation, expert evaluations, dynamic series of statistics, monographic, critical analysis, graphic tools, statistical database processing, system-structural processing of material, abstract-logical and other tools. The scale of destruction of assets of the agricultural sector and ecology as a result of Russian aggression was revealed. The institutional prerequisites for structural distortions in the agricultural sector are substantiated. The dynamics of export-import operations of the agricultural sector and alternative possibilities in the international division of labour to increase its efficiency are highlighted. Measures to restore animal husbandry by activating micro and small businesses, establishing equivalent relations of market counterparties and their observance of virtuous practices have been developed. The practical significance is reflected with a proposal on overcoming the protracted crisis in animal husbandry through an inclusive approach to the realization of the potential of micro, small, and medium-sized farms

Pineapple: The growth challenges faced by a South African Insurtech disruptor

Subject area of the teaching case: This case study is intended to supplement postgraduate business learning with the facilitation of an academic practitioner. The case draws on a culmination of subjects, and the participants are encouraged to juxtapose the case information with their professional experiences; however, the primary focus of the case material will be centred on strategy, innovation, and entrepreneurship. Student level: The primary audience for the teaching case is management education programmes including: Master of Business Administration (MBA), Postgraduate Diploma (PGDip), specialist Masters in Management, and certain Executive Education programmes. Brief overview of the teaching case: This case is about protagonist Ndabenhle Junior Ngulube, the cofounder of an innovative technology-enabled insurance intermediary company called Pineapple. The company has identified an opportunity to resolve the inherent conflict of interest within the insurance industry, as well as the grudge association of non-life insurance purchases. While the competitive landscape of the sector is traditionally dominated by a few large incumbent market participants, Pineapple's digital distribution strategy is more effective at converting ‘clicks-to-clients’, at a fraction of the typical customer acquisition cost. The peer-to-peer business model also allows for superior risk-selection, greater affinity, and lower incidents of fraudulent claims. Ndabenhle and the team develop the company's customer acquisition strategy by drawing on technological trends, reputation drivers, and a concentrated social media approach that focusses on trust, access, product, and value. But, as 2020 begins, Ndabenhle faces choices about the means and methods of scaling the business operation. The case documents the first few years of Pineapple's operations, with a strong focus on business model innovation, distribution, scalability, and technological integration. Expected learning outcomes: To analyse the role disruptive technologies play within sectoral business model innovation To evaluate the industry-specific competitive business landscape and complexities of building and maintaining a sustainable competitive advantage within a niche market segment To assess the strategic growth opportunities for an emerging market Insurtech disruptor To critically appraise the entrepreneurial complexities faced by decision-makers when looking to challenge incumbent market leaders

Influence of effusion cooling air on the CO production in the primary zone of a pressurized single sector model gas turbine combustor

In this study, the influence of effusion cooling air on the CO production in the primary zone of a pressurized single sector model gas turbine combustor is investigated in a parametric study using a combination of laser-based diagnostics. Three species (OH, CO, CH2O) are probed using laser-induced fluorescence (LIF). In principle, this combination of probed species allows to derive the forward reaction rate of CO oxidation and the local heat release rate from the LIF signals. Based on simulations and measurements in a laminar flame, we show that the limited optical resolution of the intensified cameras severely limits the applicability of the method for pressurized flames. However, by employing a conditioning strategy, mainly using CH2O-LIF as a marker for low temperature chemistry, it was shown that effusion cooling air affects the structure of the premixed flame at large distances from the effusion cooled liner. At higher blowing ratios, samples measured in the primary zone during the CO production phase exhibit a higher CO concentration at a given level of OH concentration. Most likely, this is caused by the strong non-linear sensitivity of OH concentration to temperature, shifting the local equilibrium towards higher CO concentrations, when fractions of the effusion cooling air are recirculated towards the inlet nozzle. In the exhaust phase, dilution effects seem to dominate, as a higher effusion cooling air mass flow systematically lowers both measured OH and CO signals.

Deep learning approaches to predict 10-2 visual field from wide-field swept-source optical coherence tomography en face images in glaucoma

Close monitoring of central visual field (VF) defects with 10-2 VF helps prevent blindness in glaucoma. We aimed to develop a deep learning model to predict 10-2 VF from wide-field swept-source optical coherence tomography (SS-OCT) images. Macular ganglion cell/inner plexiform layer thickness maps with either wide-field en face images (en face model) or retinal nerve fiber layer thickness maps (RNFLT model) were extracted, combined, and preprocessed. Inception-ResNet-V2 was trained to predict 10-2 VF from combined images. Estimation performance was evaluated using mean absolute error (MAE) between actual and predicted threshold values, and the two models were compared with different input data. The training dataset comprised paired 10-2 VF and SS-OCT images of 3,025 eyes of 1,612 participants and the test dataset of 337 eyes of 186 participants. Global prediction errors (MAEpoint-wise) were 3.10 and 3.17 dB for the en face and RNFLT models, respectively. The en face model performed better than the RNFLT model in superonasal and inferonasal sectors (P = 0.011 and P = 0.030). Prediction errors were smaller in the inferior versus superior hemifields for both models. The deep learning model effectively predicted 10-2 VF from wide-field SS-OCT images and might help clinicians efficiently individualize the frequency of 10-2 VF in clinical practice.

Power sector decarbonization planning considering renewable resource variability and system operational constraints

Bottom-up long-term planning models generally consider low levels of spatial, temporal, and technical details to reduce associated computation. However, these details are required to capture spatial and temporal variability of Renewable Energy Sources (RESs) and perform harmonized dispatch of conventional generating units. In their absence, models may overestimate RESs uptake and underestimate the quantum of required flexible resources. This may create a false impression of sector decarbonization and the obtained capacity mix may result in frequent RES or load curtailment during real-time system operations. In this context, this study aims to improve upon the shortcomings of conventional planning approaches and develop a TIMES based planning model for the Indian power sector. The model adopts high spatial and temporal definitions of 5 regions and 288 sub-annual timeslices. RESs are categorized into sub-classes based on annual capacity factors to capture their intra-regional variations without further increasing the spatial resolution. Each month is represented by a day of chronological hourly resolution that facilitates the capture of RES temporal variations and incorporation of operational aspects of generating units such as minimum stable generation, ramp limits and partial load operation. The impact of these operational parameters on system planning is analysed to identify a trade-off between planning accuracy and computational complexity. The study is relevant for countries that are rigorously following their decarbonization plans with high penetration of variable RESs and have a similar trajectory as that of India. The results indicate that decarbonization levels can be better assessed if capacity mix and flexible resources are adequately valued by considering operational parameters at the planning stage for a power system dominated by low flexible generation.

Neutronics Modeling and Nuclear Analysis Based on the Full CAD Geometry of CFETR

Radiation field analyses of the fusion reactor are vital to machine design and personal/environmental irradiation protection. Owing to the complicated and toroidal symmetry of fusion reactors, these nuclear analyses have been performed based on a sector model with reflecting boundary conditions. However, not all sections of a fusion reactor are symmetrical in the toroidal direction, particularly the neutron flow channels introduced by auxiliary systems from which particles can leak directly from the plasma. Hence, the reflecting boundary conditions cannot accurately describe the particle transport. Consequently, radiation field analyses based on a full-sector model must be performed to verify the results obtained. In this regard, the neutronics model of CFETR has been built in 360 deg. Meanwhile, the development of the automatic geometry conversion platform cosVMPT has enabled an entire 360-deg model of the CFETR to be established. The model contains all primary components and the outer house building. Sixteen upper/lower ports and six equatorial ports are included, in which two of them are slanted for neutral beam injection, whereas the other ports are filled with a shielding block. The on-the-fly (OTF) global variance reduction method is utilized to accelerate neutron/photon coupling transport. The results show that cosVMPT and the OTF method are reliable, and that the obtained neutron/photon flux is asymmetric outside the main machine. The computational results of the 360-deg model are compared with those of the sector model such that the application scope of simplifying the modeling and calculation using the sector model can be further confirmed.

Climate change mitigation scenario databases should incorporate more non-IAM pathways

Ajay Gambhir is a senior research fellow at the Grantham Institute, Imperial College London. His research focuses on climate change mitigation, including integrated assessment modeling, national and sectoral energy modeling, and rural electrification. He also researches low-carbon technology innovation and cost-reduction processes, as well as just transitions and climate change mitigation co-benefits. Before becoming an academic, he was a UK government economist and policy analyst, during which time he worked on the UK Climate Change Act and the development of the UK’s carbon budgets. He holds a PhD in energy economics from Imperial College London. Gaurav Ganguly is the Chief Economist for Europe and the Middle East and the Head of the Climate Scenario Group at Moodys Analytics. He specializes in creating macroeconomic scenarios that explain the transmission of a range of risks such as climate change, geopolitics, deglobalization, and fiscal and monetary policy. He has been a fellow at the Grantham Institute at Imperial College London where he developed a framework for using climate scenarios within financial institutions. He holds a DPhil in economics from the University of Oxford. Shivika Mittal is a research fellow at the Grantham Institute, Imperial College London, with eight years’ experience working on climate change mitigation. Her work focuses on assessing low-carbon pathways at global, regional, and national levels using integrated assessment models, as well as national and sectoral energy models. She was a contributing author on the low-carbon transport chapter of the IPCC’s Sixth Assessment Report on mitigation of climate change. She holds a PhD in integrated assessment modeling from the Public Systems Group at the Indian Institute of Management, Ahmedabad, India. Ajay Gambhir is a senior research fellow at the Grantham Institute, Imperial College London. His research focuses on climate change mitigation, including integrated assessment modeling, national and sectoral energy modeling, and rural electrification. He also researches low-carbon technology innovation and cost-reduction processes, as well as just transitions and climate change mitigation co-benefits. Before becoming an academic, he was a UK government economist and policy analyst, during which time he worked on the UK Climate Change Act and the development of the UK’s carbon budgets. He holds a PhD in energy economics from Imperial College London. Gaurav Ganguly is the Chief Economist for Europe and the Middle East and the Head of the Climate Scenario Group at Moodys Analytics. He specializes in creating macroeconomic scenarios that explain the transmission of a range of risks such as climate change, geopolitics, deglobalization, and fiscal and monetary policy. He has been a fellow at the Grantham Institute at Imperial College London where he developed a framework for using climate scenarios within financial institutions. He holds a DPhil in economics from the University of Oxford. Shivika Mittal is a research fellow at the Grantham Institute, Imperial College London, with eight years’ experience working on climate change mitigation. Her work focuses on assessing low-carbon pathways at global, regional, and national levels using integrated assessment models, as well as national and sectoral energy models. She was a contributing author on the low-carbon transport chapter of the IPCC’s Sixth Assessment Report on mitigation of climate change. She holds a PhD in integrated assessment modeling from the Public Systems Group at the Indian Institute of Management, Ahmedabad, India.

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Searches for dark photons at accelerators

Dark sector models attempt to describe observed dark matter properties, as well as other anomalies such as muon (g − 2), possible violations of lepton universality in B decay, and the possible observation of a new particle by the ATOMKI experiment. There is an extensive program of operating and proposed experiments to search for the new, low-mass, feebly-interacting particles predicted by such theories. I will focus on current searches at accelerators for dark photons, a component of many dark sector models. The results are interpreted in a basic model containing dark photons and dark matter.