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\"Simulating Distributional Impacts of Macro-dynamics: Theory and Practical Applications is a comprehensive guide for analyzing and understanding the effects of macroeconomic shocks on income and consumption distribution, as well as for using the ADePT Simulation Module. Since real-time micro data is rarely available, the Simulation Module (part of the ADePT economic analysis software) takes advantage of historical household surveys to estimate how current or proposed macro changes might impact household and individual welfare\"--Back cover.

The automated DEC poverty tables (ADePT) simulation module, one of several modules in the ADePT platform, offers a useful methodological framework for analysts interested in measuring how macroeconomic projections may affect households. The modules approach falls between simple extrapolation and the most sophisticated methods such as top-down or top-down-up models based on linking household data with computable general equilibrium (CGE) models. By using simple macroeconomic projections as the macro-linkages to a micro-behavioral model built from household data, the model captures the complexities that influence how macro impacts are transmitted to households. The ADePT simulation module is an improvement over existing approaches because with minimal data and computational requirements it can evaluate in advance the distributional impacts of macroeconomic projections. By focusing on adjustments in employment and earnings, non-labor income, and price changes, it accounts for multiple transmission mechanisms and captures micro-level impacts across the entire income distribution. Using existing macroeconomic data and household surveys, the ADePT simulation module helps in identifying and profiling those groups of individuals - defined by characteristics such as occupational sector, location, and education level who are most likely to suffer income losses as a consequence of the change. This manual is organized in two parts. Part one covers the motivation, overview, and illustrations of the method. Part two describes each step the user must follow to create or obtain proper macro- and microeconomic inputs required for the simulation. It also explains how to enter these inputs into the module and the different options available for tailoring simulations.

Living in an area with high levels of child poverty predisposes children to poorer mental and physical health. ActEarly is a 5-year research programme that comprises a large number of interventions (>20) with citizen science and co-production embedded. It aims to improve the health and well-being of children and families living in two areas of the UK with high levels of deprivation; Bradford in West Yorkshire, and the London Borough of Tower Hamlets. This protocol outlines the meta-evaluation (an evaluation of evaluations) of the ActEarly programme from a systems perspective, where individual interventions are viewed as events in the wider policy system across the two geographical areas. It includes investigating the programme's impact on early life health and well-being outcomes, interdisciplinary prevention research collaboration and capacity building, and local and national decision making. The ActEarly meta-evaluation will follow and adapt the five iterative stages of the 'Evaluation of Programmes in Complex Adaptive Systems' (ENCOMPASS) framework for evaluation of public health programmes in complex adaptive systems. Theory-based and mixed-methods approaches will be used to investigate the fidelity of the ActEarly research programme, and whether, why and how ActEarly contributes to changes in the policy system, and whether alternative explanations can be ruled out. Ripple effects and systems mapping will be used to explore the relationships between interventions and their outcomes, and the degree to which the ActEarly programme encouraged interdisciplinary and prevention research collaboration as intended. A computer simulation model (\"LifeSim\") will also be used to evaluate the scale of the potential long-term benefits of cross-sectoral action to tackle the financial, educational and health disadvantages faced by children in Bradford and Tower Hamlets. Together, these approaches will be used to evaluate ActEarly's dynamic programme outputs at different system levels and measure the programme's system changes on early life health and well-being. This meta-evaluation protocol presents our plans for using and adapting the ENCOMPASS framework to evaluate the system-wide impact of the early life health and well-being programme, ActEarly. Due to the collaborative and non-linear nature of the work, we reserve the option to change and query some of our evaluation choices based on the feedback we receive from stakeholders to ensure that our evaluation remains relevant and fit for purpose.

Resource-based livelihoods are uncertain and potentially unstable due to variability over time, including seasonal variation: this instability threatens marginalised populations who may fall into poverty. However, empirical understanding of trajectories of household well-being and poverty is limited. Here, we present a new household-level model of poverty dynamics based on agents and coping strategies-the Household Economy And Poverty trajectory (HEAP) model. HEAP is based on established economic and social insights into poverty dynamics, with a demonstration of the model calibrated with a qualitative and quantitative household survey in coastal Bangladesh. Economic activity in Bangladesh is highly dependent on natural resources; poverty is widespread; and there is high variability in ecosystem services at multiple temporal scales. The results show that long-term decreases in poverty are predicated more on the stability of, and returns from, livelihoods rather than their diversification. Access to natural resources and ecosystem service benefits are positively correlated with stable income and multidimensional well-being. Households that remain in poverty are those who experience high seasonality of income and are involved in small scale enterprises. Hence, seasonal variability in income places significant limits on natural resources providing routes out of poverty. Further, projected economic trends to 2030 lead to an increase in well-being and a reduction in poverty for most simulated household types.

To date there has been limited analysis of the economic costs and benefits associated with cannabis legalisation. This study redresses this gap. A cost benefit analysis of two cannabis policy options the status quo (where cannabis use is illegal) and a legalised-regulated option was conducted. A cost benefit analysis was used to value the costs and benefits of the two policies in monetary terms. Costs and benefits of each policy option were classified into five categories (direct intervention costs, costs or cost savings to other agencies, benefits or lost benefits to the individual or the family, other impacts on third parties, and adverse or spill over events). The results are expressed as a net social benefit (NSB). The mean NSB per annum from Monte Carlo simulations (with the 5 and 95 percentiles) for the status quo was $294.6 million AUD ($201.1 to $392.7 million) not substantially different from the $234.2 million AUD ($136.4 to $331.1 million) for the legalised-regulated model which excludes government revenue as a benefit. When government revenue is included, the NSB for legalised-regulated is higher than for status quo. Sensitivity analyses demonstrate the significant impact of educational attainment and wellbeing as drivers for the NSB result. Examining the percentiles around the two policy options, there appears to be no difference between the NSB for these two policy options. Economic analyses are essential for good public policy, providing information about the extent to which one policy is substantially economically favourable over another. In cannabis policy, for these two options this does not appear to be the case.

Ecological risk refers to the potential threat that landscape changes pose to ecosystem structure, function, and service sustainability, while ecological security emphasizes the ability of regional ecosystems to maintain stability and support human well-being. Developing an Ecological Security Pattern (ESP) provides a strategic approach to balance ecological protection and sustainable development. This study investigates the spatial and temporal dynamics of landscape ecological risk in the Tarim Basin and surrounding urban areas in the Xinjiang Uygur Autonomous Region, China, from 2000 to 2020. Using a combination of the InVEST model, landscape connectivity index, and circuit theory-based modeling, we identify ecological source areas and simulate ecological corridors. Ecological source areas are categorized by their ecological value and connectivity: primary sources represent high ecological value and strong connectivity, secondary sources have moderate ecological significance, and tertiary sources are of relatively lower priority but still vital for regional integrity. The results show a temporal trend of ecological risk declining between 2000 and 2010, followed by a moderate increase from 2010 to 2020. High-risk zones are concentrated in the central Tarim Basin, reflecting intensified land-use pressures and weak ecological resilience. The delineated ecological protection zones include 61,702.9 km2 of primary, 146,802.5 km2 of secondary, and 36,141.2 km2 of tertiary ecological source areas. In total, 95 ecological corridors (23 primary, 37 secondaries, and 35 tertiary) were identified, along with 48 pinch points and 56 barrier points that require priority attention for ecological restoration. Valuable areas refer to those with high ecological connectivity and service provision potential, while vulnerable areas are characterized by high ecological risk and landscape fragmentation. This study provides a comprehensive framework for constructing ESPs in arid inland basins and offers practical insights for ecological planning in desert–oasis environments.

Current shale gas well production capacity predictions primarily rely on analytical and numerical simulation methods, which necessitate extensive calculations and manual parameter tuning and produce lowly accurate predictions. Although employing neural networks yields highly accurate predictions, they can easily fall into local optima. This paper suggests a new way to use Cuckoo Search (CS)-optimized neural networks to make shale gas well production capacity predictions more accurate and to solve the problem of local optima. It aims to assist engineers in devising more effective development plans and production strategies, optimizing resource allocation, and reducing risk. The method first analyzes the factors influencing the production capacity of shale gas wells in a block located in western China through correlation coefficients. It identifies the main factors affecting the gas test absolute open flow as organic carbon content, small-layer passage rate, fracture pressure, acid volume, pump-in fluid volume, brittle mineral content in the rock, and rock density. Subsequently, we used the CS algorithm to conduct the global training of the neural network, avoiding the problem of local optima, and established a neural network model for predicting shale gas well production capacity optimized by the CS algorithm. A comparative analysis with other relevant methods demonstrates that the CS-optimized neural network model can accurately predict production capacity, enabling a more rational and effective exploitation of shale gas resources, which lower development costs and increase the economic returns of oil and gas fields. Compared to numerical simulation, SVM, and BP neural network algorithms, the CS-optimized BP neural network (CS-BP) exhibits significantly lower prediction error. Its correlation coefficient between predicted and actual values reaches as high as 0.9924. Verification experiments conducted on another shale gas well also demonstrate that, in comparison to the BP neural network algorithm, CS-BP offers superior prediction performance, with model validation showing a prediction error of only 0.05. This study can facilitate more rational and efficient exploitation of shale gas resources, reduce development costs, and enhance the economic benefits of oil and gas fields.

As a vital bridge linking human well-being with ecological processes, cultural ecosystem services (CESs) play a pivotal role in understanding the equilibrium of social–ecological systems. However, the spatial supply–demand relationships of CESs remain underexplored in rapidly urbanizing regions. This study establishes an integrated framework by synthesizing multi-source geospatial data, socioeconomic indicators, and the Maximum Entropy (MaxEnt) model to investigate the spatial dynamics of CESs in Taiyuan City. Key findings include the following: (1) A pronounced spatial heterogeneity in CES supply distribution, exhibiting a core-to-periphery diminishing gradient, with inverse correlations observed among different CES categories. (2) Accessibility, topographic features, and fractional vegetation cover emerged as primary drivers of spatial supply differentiation, while climatic factors and elevation exerted non-negligible influences on this Loess Plateau urban system. (3) Four spatial mismatch patterns were identified through the supply–demand analysis: high supply–high demand (38.1%), low supply–low demand (37.2%), low supply–high demand (13.6%), and high supply–low demand (10.9%). The coupling coordination degree of CESs in Taiyuan City indicated moderate coordination, with severe imbalances observed in urban–rural transitional zones. This study reveals nonlinear interactions between natural landscapes and anthropogenic factors in shaping CES spatial distributions, particularly the trade-offs between esthetic value and transportation constraints. By integrating big data and spatial modeling, this research advances CES quantification methodologies and provides actionable insights for optimizing green infrastructure, prioritizing ecological restoration, and balancing urban–rural CES provision. These outcomes address methodological gaps in coupled social–ecological system research while informing practical spatial governance strategies.

The Fraser River Basin (FRB) of British Columbia is one of the largest and most important watersheds in western North America, and home to a rich diversity of biological species and economic assets that depend implicitly upon its extensive riverine habitats. The hydrology of the FRB is dominated by snow accumulation and melt processes, leading to a prominent annual peak streamflow invariably occurring in May–July. Nevertheless, while annual peak daily streamflow (APF) during the spring freshet in the FRB is historically well correlated with basin-averaged, 1 April snow water equivalent (SWE), there are numerous occurrences of anomalously large APF in below- or near-normal SWE years, some of which have resulted in damaging floods in the region. An imperfect understanding of which other climatic factors contribute to these anomalously large APFs hinders robust projections of their magnitude and frequency. We employ the Variable Infiltration Capacity (VIC) process-based hydrological model driven by gridded observations to investigate the key controlling factors of anomalous APF events in the FRB and four of its subbasins that contribute nearly 70 % of the annual flow at Fraser-Hope. The relative influence of a set of predictors characterizing the interannual variability of rainfall, snowfall, snowpack (characterized by the annual maximum value, SWEmax), soil moisture and temperature on simulated APF at Hope (the main outlet of the FRB) and at the subbasin outlets is examined within a regression framework. The influence of large-scale climate modes of variability (the Pacific Decadal Oscillation (PDO) and the El Niño–Southern Oscillation – ENSO) on APF magnitude is also assessed, and placed in context with these more localized controls. The results indicate that next to SWEmax (univariate Spearman correlation with APF of ρ^ = 0.64; 0.70 (observations; VIC simulation)), the snowmelt rate (ρ^ = 0.43 in VIC), the ENSO and PDO indices (ρ^ = −0.40; −0.41) and (ρ^ = −0.35; −0.38), respectively, and rate of warming subsequent to the date of SWEmax (ρ^ = 0.26; 0.38), are the most influential predictors of APF magnitude in the FRB and its subbasins. The identification of these controls on annual peak flows in the region may be of use in understanding seasonal predictions or future projected streamflow changes.

\"Simulating Distributional Impacts of Macro-dynamics: Theory and Practical Applications is a comprehensive guide for analyzing and understanding the effects of macroeconomic shocks on income and consumption distribution, as well as for using the ADePT Simulation Module. Since real-time micro data is rarely available, the Simulation Module (part of the ADePT economic analysis software) takes advantage of historical household surveys to estimate how current or proposed macro changes might impact household and individual welfare\"--Back cover