Adaptation Costs Research Articles

An Exogenous Risk in Fiscal-Financial Sustainability: Dynamic Stochastic General Equilibrium Analysis of Climate Physical Risk and Adaptation Cost

This research aims to explore the fiscal and public finance viability on climate physical risk externalities cost for building social-economic-environmental sustainability. It analyzes climate physical risk impact on the real business cycle to change the macroeconomic output functions, its regressive cyclic impact alters tax revenue income and public expenditure function; This research also analyzes that the climate physical risk escalates social-economic inequality and change fiscal-financial policy functions, illustrates how the climate damage cost and adaptation cost distorts fiscal-finance cyclical and structural equilibrium function. This research uses binary and multinomial logistic regression analysis, dynamic stochastic general equilibrium method (DSGE) and Bayesian estimation model. Based on the climate disaster compensation scenarios, damage cost and adaptation cost, analyzing the increased public expenditure and reduced revenue income, demonstrates how climate physical risk externalities generate binary regression to financial fiscal equilibrium, trigger structural and cyclical public budgetary deficit and fiscal cliff. This research explores counterfactual balancing measures to compensate the fiscal deficit from climate physical risk: effectively allocating resources and conducting the financial fiscal intervention, building greening fiscal financial system for creating climate fiscal space.

Integration of a Belt Starter Generator in a Flex-Fuel Vehicle

<div>The concern with global warming has led to the creation of legislation aimed at minimizing this phenomenon. As a result, the development of technologies to minimize vehicle emissions and reduce fuel consumption has gained market share. A promising alternative is the use of a belt starter generator (BSG): an electric machine to replace the vehicle’s alternator. This research analyzes the effects of introducing a 12 V BSG into a flex-fuel vehicle, specifically examining its impact on fuel economy and CO<sub>2</sub> emissions when using both gasoline and ethanol. The utilization of a low-voltage BSG in a flex-fuel vehicle has not been previously studied. Numerical simulations and experimental fuel consumption and CO<sub>2</sub> emissions tests were performed for the normal production flex-fuel baseline configuration and the vehicle with the 12 V BSG, following the standards ABNT NBR 6601 and ABNT NBR 7024. The use of the BSG led to a 10.06% reduction in CO<sub>2</sub> emission in the urban cycle for the vehicle running on gasoline and a 6.28% reduction in energy consumption in the combined cycle. The results demonstrated that the low-voltage BSG is a promising solution for reducing fuel consumption and GHG emissions in flex-fuel vehicles. The electrical machine installation required minimal modifications to the vehicle and had a low adaptation cost. The BSG can also improve vehicle performance and drivability.</div>

The influence of breathing exercises on the heart rate variability parameters of female medical students

We studied the influence of breathing exercises (BE) with an imposed rhythm on the dynamics of heart rate variability (HRV) parameters in order to study the nature of adaptive processes in the body of girls with different initial levels of total HRV. Heart rate, time indicators: SDNN, mNN, RMSSD, pNN50 - and spectral parameters: TP, HF and HFnu, LF and LFnu, LF/HF - as well as IVR and IN indices were evaluated. According to the level of SDNN, individuals with conditionally low and conditionally high levels of total HRV were identified - groups LV (40 (31; 47) ms) and HV (64 (58; 70) ms). At rest, HRV parameters in HV-girls were significantly higher than in the LV-subgroup. After BE, a significant increase in SDNN and TP was observed in the LV-subgroup, against the background of a decrease in HF and HFnu and an increase in LF and LFnu, and the LF/HF ratio after BE increased by 80%. In HV-girls after BE, an increase of 6% in heart rate was recorded, but not in total HRV parameters: a decrease in HF and HFnu was compensated by an increase in LFnu, as a result, the LF/HF ratio increased significantly by almost 1.5 times compared to the rest level. In LV-girls, the indicators of autonomic balance index (AVR) and stress-index (SI) were higher by 2.6–2.7 than in the HV-subgroup. After BE, a decrease of 24 and 19% in the parameters of AVR and SI was observed in the LV, but not in the HV-subgroup. Thus, the adaptation of the work of the heart to BE occurred in a similar way in LV- and HV-individuals, however, it differed in different adaptive costs of such an effort for LV- and HV-subjects.

Evaluating Accessibility Solutions in Collective Residential Buildings: Field Research in Southeast Spain

With the ageing of the population in Western countries, the prevalence of disability and mobility problems is increasing, highlighting the urgent need to improve accessibility in environments where people spend a significant amount of time, such as collective housing. This paper examines the accessibility of building entrances in collective housing in the Region of Murcia, south-eastern Spain, where 9.8% of the population is estimated to live with disabilities. Starting with a thorough review of national and regional accessibility regulations, this study applies a robust methodology by conducting fieldwork in 150 buildings to assess compliance and identify barriers. The methodology involved a systematic assessment of the accessibility of entrances, using criteria derived from the regulations, and a specific proposal of the accessibility solutions required for each case. The key findings show that the most effective way for improving the accessibility is a properly constructed ramp, with over 40% of buildings requiring the installation or improvement of ramps, either as a stand-alone solution or in combination with other adaptations. In 54% of cases, a multi-faceted approach was required to meet accessibility standards. It was also noted that older buildings typically require higher adaptation costs. Based on these findings, the study provides specific recommendations, such as the construction of ramps and other critical interventions, to improve the accessibility of buildings. These recommendations have the potential to guide public policy and drive improvements in urban planning to make residential areas more accessible.

Hyperparasitic showdown: <i>Sclerodermus cereicollis</i>, a non-aggressive but surprisingly secondary hyperparasitoid

This study investigates the dynamics of hyperparasitism within the Bethylidae family, focusing on the interaction between Sclerodermus cereicollis and Goniozus legneri, two parasitoid wasp species characterized by different sociality and aggressivity towards conspecifics. Experimental trials were conducted using Corcyra cephalonica larvae as hosts, with different setups to stimulate competition. The results revealed unexpected behaviors, especially by S. cereicollis, including aggressive interactions, cannibalism, and hyperparasitism. In contrast, despite the typically aggressive nature of G. legneri, no particular hostility was observed toward S. cereicollis. The study highlights the complex dynamics of competition for resources among parasitoids, shedding light on the adaptive strategies and fitness costs associated with hyperparasitism. These findings contribute to a deeper understanding of the ecological interactions within parasitoid wasp communities.

Can markets for nature conservation be successful? An integrated assessment of a product label for biodiversity practices in Germany

Biodiversity is declining in many ecosystems, particularly in agricultural landscapes. So far, instruments such as the Common Agricultural Policy (CAP) have not been able to halt this trend. Stricter legal regulations tend to be poorly received by farmers and this leads to conflicts. However, their acceptance is crucial as agriculture is a major stakeholder in biodiversity conservation. In this context, market-based approaches are alternative or complementary instruments for biodiversity conservation. However, there are few examples in Germany and the potential economic feasibility on a larger scale has hardly been investigated. We, therefore, analyzed the economic and agronomic impacts of a potential new market label for biodiversity-friendly products on conventional arable farms in two German case study regions – Brandenburg and North Rhine-Westphalia (NRW) – with different agricultural frameworks. The label requirements were defined in advance by a group of experts in the field of nature conservation. In terms of methodology, we combined a survey of nine companies in the food sector, for instance food retailers, that looked at potential price premium scenarios for products with the label and economic land use modelling. The participation of farms led to an average reduction of gross margins per hectare of arable land of approximately 64 € in Brandenburg and about 96 € in NRW. This revealed spatial disparities. Regarding biodiversity impacts, pesticide use was reduced by about 50 % in both regions. This indicated a high level of effectiveness coupled with comparably moderate farm adaptation costs. Price premiums under the market label for potatoes, wheat, and rye averaging 10 % could almost compensate for the loss of farm income in both regions. Premiums between 5 % and 10 % were also the order of magnitude estimated by the companies that took part in the survey. At the same time, the survey did show that potential transaction costs could be quite high. We, therefore, concluded that the market alone was unlikely to finance the introduction of the label in the case study regions. There was a need for accompanying instruments such as the CAP or private business investment. However, given the case study character, the results should be interpreted with caution and cannot be considered representative of all German or European agricultural conditions. They require further research. Nevertheless, our results are highly relevant for policy makers, as they show that biodiversity conservation, at least in the study regions considered, cannot be tackled with one single instrument, but requires a mix of different ones. For instance, a combination of policies and private initiatives, such as the CAP and a private market label like the one considered in the study.

Identifikasi dan Mitigasi Risiko pada Proyek Konstruksi Menggunakan Metode House of Risk: Studi Kasus

PT. Rigis Beukarya Property is a developer and contractor for housing projects in the West Aceh region. Housing projects that are implemented often experience obstacles in the construction of the project, such as over budget from the predetermined RAB, repeated repairs, materials that are ordered late, and exposure to bad weather which results in large losses due to these obstacles. The aim of this research is to identify risks in the Grand Keutapang housing development process, and mitigate the dominant risks in the Grand Keutapang housing development process. The House of risk (HOR) method is used to solve this problem by identifying risk events, risk agents and also designing risk agent mitigation strategies based on Aggregate Risk Potential (ARP) values. The results obtained from the house of risk stage 1 risk mapping show that there are 5 dominant risk agents that will be taken into consideration in developing mitigation actions, namely: A-14 (Lack of coordination between parties involved) with an ARP value of 3,384, A-1 (Incompetent/careful workforce) with an ARP value of 1,800, A-12 (Fluctuating company financial cash flow) with an ARP value of 826 , A-15 (Lack of supervision in the field) with an ARP value of 777, and A-8 (Increase in material prices) with an ARP value of 696. From the results of the house of risk phase 2 risk mapping, 5 risk mitigation action plans were obtained, including: PA-1 (Creating a monitoring and sanctions system) with an ETDk value of 5,961, PA-2 (Creating a standard diary (buhas)) with an ETDk value of 3,331, PA-3 (Creating a complete checklist) with an ETDk value of 6,236, PA - 4 (Making adaptive cost estimates) with an ETDk value of 4,270, and PA-5 (Improving the effectiveness of communication with all parties) with an ETDk value of 8.427.

Linking perceptions of climate change impacts with adaptation: Insights from landowners in Southern Chile

Adaptation is recognized as the outcome of a complex mix of individual and institutional factors that shape how society responds to climate change. Adaptation results from a joint decision-making process, where actors simultaneously evaluate the risks associated with climate change into whether or not they should adapt. We develop a model of this joint decision-making process that incorporates risk tolerance to identify what factors influence landowners’ perception and adaptation to climate change in southern Chile. The results are based on 86 in-person interviews, involving the collection of socioeconomic data, risk aversion tests, and semi-structured interviews. We found that while most landowners perceived climate change impacts as a threat, only 60 % had taken any action. Two underlying factors applied to both perception and adaptation: risk tolerance and off-farm incomes. Higher risk tolerance and greater reliance on off-farm incomes reduced people's perception and adaptation to climate change. The presence of climate change-induced impacts positively influenced the implementation of adaptation, while schooling and gender were relevant only in shaping climate change perceptions. Following these results, we suggest developing programs to communicate the real magnitude of climate risks so that landowners better understand the opportunity costs of climate change adaptation, and in that way, avoid/anticipate the need to see impacts on the land in order to act. Along these lines, further investigation of the role off-farm incomes play in adaptation is warranted, where it is simultaneously both a factor in the adaptation process but can also be an adaptation action as well.

Techno-economic evaluation of transesterification processes for biodiesel production from low quality non-edible feedstocks: Process design and simulation

The global demand for fossil fuels has led to increased pollutant emissions and depleted fossil fuel resources. Biodiesel, a fossil fuel alternative, is widely produced via transesterification. This study assesses the techno-economic performances of three transesterification processes (alkaline, acid, and CaO catalytic) for biodiesel production from low-quality non-edible feedstocks. The study explores effects of elevated free fatty acids (FFAs) and oil/ethanol flow rates on these processes, focusing on their impact on yield, purity, economics and energy aspects. Aspen Plus® V10 software was used for simulations. Despite meeting international biodiesel standards, significant technical and economic variations exist among the processes. The acid catalytic process exhibits energy requirements surpassing those of alkaline and CaO catalytic processes by over 29.58%, leading to operational costs exceeding those of CaO catalysis by 13.11%. The study establishes CaO catalysis as the most feasible option due to its simplicity, adaptability, and substantial energy and cost reductions. By introducing a closed-loop blending setup configuration, the study reveals that CaO catalysis outperforms alkaline and acid catalysis, achieving 11.59% cost reduction and 13.31% energy decrease in closed-loop configurations. The overall results highlight the potential of non-edible feedstocks in biodiesel production for a more environmentally friendly and sustainable energy future.

Estimating adaptation effort in industry 4.0-enabled systems: Introducing two complexity indices with an evolvable network graph approach

One of the key aims of Industry 4.0 is to create more responsive systems. Responsiveness enables coping with new market requirements or introducing new products, as demonstrated by the COVID-19 challenges. However, there are currently no effective methods for measuring the responsiveness or reconfigurability of a system, or for quantifying the effort required to adapt it from one state to another. Adapting a production cell from its current state to a new adapted state requires a significant amount of information about dismantling, reintegrating, and handling physical equipment, as well as updating the software controller. Practitioners often only consider adaptation options for simple process parametrization or at the end of a system's life cycle, overlooking many potential adaptation opportunities. This paper proposes an evolvable network graph approach for supporting reconfiguration decisions by estimating the effort required to adapt the physical structure. Two complexity indexes have been developed to quantify the adaptation activities. An estimation algorithm infers the effort from the difference in the adaptation graphs that represent alternative options. The approach is illustrated in a laboratory-scale cell and applied in two industrial-sized cells, quantifying adaptation times of approximately 58, 7, and 122 h, respectively. This is equivalent to £3129.6, £356.04, and £6118.8, utilizing average hourly rates for system integrators and equipment handlers. The results show that the approach can effectively quantify the adaptation effort for different equipment sizes and connections, estimating the adaptation cost and time from the graph change quickly at around a millisecond and with minimal computational resources.

Dual-domain joint optimization for universal JPEG steganography

JPEG steganography hides secret messages by modifying DCT coefficients in cover images to ensure the undetectability. Currently, most JPEG steganography methods rely on the additive distortion model, assigning adaptive costs to individual coefficients. However, these costs are often designed within a single domain, either DCT or decompressed domain, thereby making the hiding behavior detectable by multi-domain steganalysis features. To improve the steganography security, this paper proposes an universal Dual-Domain JPEG Additive Distortion (DDJAD) framework, which minimize additive distortion in both JPEG and decompressed domain concurrently. A measure to reduce the perceptibility of coefficient modifications within pixel blocks is first formulated and then incorporated into the traditional JPEG steganography model. Finally, the new additive distortion model is solved by fine-tuning existing costs, illustrating the universality of the proposed method. Experimental results showcase the superiority of the proposed method over previous works across various quality factors and payloads, employing typical steganalysis features.

Adaptive responses to living in stressful habitats: Do invasive and native plant populations use different strategies?

Plants inhabit stressful environments characterized by a variety of stressors, including mine sites, mountains, deserts, and high latitudes. Populations from stressful and reference (non-stressful) sites often have performance differences. However, while invasive and native species may respond differently to stressful environments, there is limited understanding of the patterns in reaction norms of populations from these sites. Here, we use phylogenetically controlled meta-analysis to assess the performance of populations under stress and non-stress conditions. We ask whether stress populations of natives and invasives differ in the magnitude of lowered performance under non-stress conditions and if they vary in the degree of performance advantage under stress. We also assessed whether these distinctions differ with stress intensity. Our findings revealed that natives not only have greater adaptive advantages but also more performance reductions than invasives. Populations from very stressful sites had more efficient adaptations, and performance costs increased with stress intensity in natives only. Overall, the results support the notion that adaptation is frequently costless. Reproductive output was most closely associated with adaptive costs and benefits. Our study characterized the adaptive strategies used by invasive and native plants under stressful conditions, thereby providing important insights into the limitations of adaptation to extreme sites.

Predicting climate smart agriculture (CSA) practices using machine learning: A prime exploratory survey

The paper aim and novelty is the development of technology-based tools able of providing realistic insights on farmers’ future adaptation decisions by developing an ML algorithm to predict Climate-Smart Agriculture (CSA) practices and highlight modeling challenges to account for. And proposing a theoretical approach that grounds the selection of data (i.e. input and response variables) with well stablished theories on adaptation decision making process; with the aim of demonstrating ways of improving data science and ML publication quality in the field of agricultural economics. Data used are farmers’ socio-economic characteristics, farms’ features, agro-ecology’s features, climate indicators (temperature, rain, etc.), etc. In this paper, the optimized Gradient Boosting ML was trained and tested using households’ level data from Rakai district in Central Region of Uganda. The modeling approach was framed in climate adaptation analytical frameworks. Data extracted allows generating CSA clusters giving two response variables (i.e. yCSA_pratices and yCSA_clusters), used separately to train two different algorithms. The developed CSA predictive algorithm demonstrates that adaptation practices can be predicted using households’ level parameters. And both models are revealed to have fair performance metrics, with yCSA_clusters algorithm reaching up to 60% of accuracy. To further improve accuracy scores, deep-learning algorithms are suggested in future research. The developed CSA prediction algorithm could be used at both households and value chain levels, to select appropriate adaptation strategies, to plan adaptation, to estimate adaptation costs and develop investment’ plans.

Quantifying adaptation costs in sequential FDI location choices: Evidence from German firms

Initiating foreign direct investment (FDI) is expensive for multinational firms due to the need to adapt to new business practices, ethical norms, and regulations in a foreign country. This paper examines how such adaptation costs affect firms' FDI decisions in current and subsequent periods. We develop a dynamic structural model of how firms make sequential decisions regarding where to invest. Using unique data covering all German firms' FDI from 2002 to 2009, we estimate the model that allows for country-specific adaptation costs and firms' heterogeneous preferences for location attributes. The estimation results suggest that the adaptation costs are statistically and economically significant, ranging from 0.9% to 22.4% of a firm's average expected discounted profits. If adaptation costs were completely subsidized, firms' FDI location choices would change drastically. Moreover, the average expected discounted profit would increase by 10.9%, not only because of the reduction in adaptation costs but more importantly, due to better matching between firms and locations.

Development of an evaluation indicator for highway climate change adaptation projects based on analytical hierarchy process in South Korea

AbstractOwing to climate change, abnormal climate phenomena occur frequently, damaging aging highway facilities. Therefore, it is necessary to develop adaptation projects for highway systems. Adaptation projects focus on mitigating the impact of climate change on highway facilities and enabling safe use. The present study aims to develop quantified evaluation indicators that are necessary for planning and selecting appropriate climate change adaptation projects. By analysing previous studies, the most important factors to be considered in the evaluation process of climate change adaptation projects were defined, and 20 preliminary evaluation indicators were divided into three layers. A feasibility study for each class was performed on the preliminary evaluation indicators by an expert panel; 16 evaluation indicators were selected through a feasibility study, and the weight of each indicator was calculated using the analytical hierarchy process (AHP) method. The priority of the evaluation indicators was service improvement through adaptation projects (weight 8.76), cost reduction and job creation (weight 8.46), and climate change impact reduction and vulnerability reduction (weight 8.31). These quantified evaluation indicators can help organizations that manage highways respond to climate change and establish adaptation projects.

On the genetic basis of tail-loss evolution in humans and apes

The loss of the tail is among the most notable anatomical changes to have occurred along the evolutionary lineage leading to humans and to the ‘anthropomorphous apes’1–3, with a proposed role in contributing to human bipedalism4–6. Yet, the genetic mechanism that facilitated tail-loss evolution in hominoids remains unknown. Here we present evidence that an individual insertion of an Alu element in the genome of the hominoid ancestor may have contributed to tail-loss evolution. We demonstrate that this Alu element—inserted into an intron of the TBXT gene7–9—pairs with a neighbouring ancestral Alu element encoded in the reverse genomic orientation and leads to a hominoid-specific alternative splicing event. To study the effect of this splicing event, we generated multiple mouse models that express both full-length and exon-skipped isoforms of Tbxt, mimicking the expression pattern of its hominoid orthologue TBXT. Mice expressing both Tbxt isoforms exhibit a complete absence of the tail or a shortened tail depending on the relative abundance of Tbxt isoforms expressed at the embryonic tail bud. These results support the notion that the exon-skipped transcript is sufficient to induce a tail-loss phenotype. Moreover, mice expressing the exon-skipped Tbxt isoform develop neural tube defects, a condition that affects approximately 1 in 1,000 neonates in humans10. Thus, tail-loss evolution may have been associated with an adaptive cost of the potential for neural tube defects, which continue to affect human health today.

Enhanced Robot Motion Block of A-Star Algorithm for Robotic Path Planning.

An optimized robot path-planning algorithm is required for various aspects of robot movements in applications. The efficacy of the robot path-planning model is vulnerable to the number of search nodes, path cost, and time complexity. The conventional A-star (A*) algorithm outperforms other grid-based algorithms because of its heuristic approach. However, the performance of the conventional A* algorithm is suboptimal for the time, space, and number of search nodes, depending on the robot motion block (RMB). To address these challenges, this paper proposes an optimal RMB with an adaptive cost function to improve performance. The proposed adaptive cost function keeps track of the goal node and adaptively calculates the movement costs for quickly arriving at the goal node. Incorporating the adaptive cost function with a selected optimal RMB significantly reduces the searches of less impactful and redundant nodes, which improves the performance of the A* algorithm in terms of the number of search nodes and time complexity. To validate the performance and robustness of the proposed model, an extensive experiment was conducted. In the experiment, an open-source dataset featuring various types of grid maps was customized to incorporate the multiple map sizes and sets of source-to-destination nodes. According to the experiments, the proposed method demonstrated a remarkable improvement of 93.98% in the number of search nodes and 98.94% in time complexity compared to the conventional A* algorithm. The proposed model outperforms other state-of-the-art algorithms by keeping the path cost largely comparable. Additionally, an ROS experiment using a robot and lidar sensor data shows the improvement of the proposed method in a simulated laboratory environment.

GAN-based adaptive cost learning for enhanced image steganography security

A well-defined cost function is a key issue for image steganography to minimize the embedding distortion. In recent years, deep learning has been introduced into image steganography to automatically learn embedding costs and improve steganographic security. For most existing generative adversarial network (GAN) based cost learning works, the generator usually adopts an encoder–decoder architecture. However, due to repeated encoding and decoding operations, this architecture is prone to information loss, making the generator difficult to well capture fine-grained image features. In this work, we propose a novel GAN-based image steganography work that improves the cost function by learning better embedding probability maps. Specifically, we design an attention mechanism to be integrated into the U-Net architecture, which enables the generator to concentrate on texture-rich regions of input images. Moreover, an extra input stream, namely the enhanced image, is introduced into the generator, improving the generator’s ability to learn structural features from input images. Different skip connections are used for different input streams to facilitate information flow between different layers. Extensive experimental results demonstrate that the proposed approach effectively learns the embedding probability maps and achieves superior security against various steganalysis attacks.

Vulnerability to Climate Change: Adaptation Costs of Artisanal Sea Fishing in Lima Region, Peru

Artisanal fishers and their livelihoods are vulnerable to variable catches caused mainly by climate change. The objective of this study is to assess vulnerability and estimate the costs of adaptation to climate change among artisanal fishers in the Lima region. The method consisted of generating catch loss indices and adaptation measures for artisanal marine fishing based on workshops with local fishers. According to the seawater surface temperature anomalies (SSTA) metric recorded by coastal laboratories of the Peruvian sea, the El Niño phenomenon in the summer of 2017 was classified as strong. We constructed five-catch variation indices (from ‘slight’ to ‘severe’ catch loss variation). The results indicate that in the artisanal fishery, despite there being no species subject to severe variation, there were some, such as flathead mullet (Mugil cephalus), mahi mahi (Coryphaena hippurus), and crab (Platyxanthus orbignyi), with a very high variation index, while the snakehead kingcroaker (Menticirrhus ophicephalus) exhibited moderate variation. We concluded that for the period 2017–2030, the adaptation costs (base year 2017) of accessible fishing credit and a bonus payment to artisanal fishers affected by El Niño events would be 3.9 million soles (US $ 1.2 million) in 2017 present value.

Costs of molecular adaptation to the chemical exposome: a focus on xenobiotic metabolism pathways.

Organisms adapt to their environment through different pathways. In vertebrates, xenobiotics are detected, metabolized and eliminated through the inducible xenobiotic-metabolizing pathways (XMP) which can also generate reactive toxic intermediates. In this review, we will discuss the impacts of the chemical exposome complexity on the balance between detoxication and side effects. There is a large discrepancy between the limited number of proteins involved in these pathways (few dozens) and the diversity and complexity of the chemical exposome (tens of thousands of chemicals). Several XMP proteins have a low specificity which allows them to bind and/or metabolize a large number of chemicals. This leads to undesired consequences, such as cross-inhibition, inefficient metabolism, release of toxic intermediates, etc. Furthermore, several XMP proteins have endogenous functions that may be disrupted upon exposure to exogenous chemicals. The gut microbiome produces a very large number of metabolites that enter the body and are part of the chemical exposome. It can metabolize xenobiotics and either eliminate them or lead to toxic derivatives. The complex interactions between chemicals of different origins will be illustrated by the diverse roles of the aryl hydrocarbon receptor which binds and transduces the signals of a large number of xenobiotics, microbiome metabolites, dietary chemicals and endogenous compounds. This article is part of the theme issue 'Endocrine responses to environmental variation: conceptual approaches and recent developments'.