Input-output Model Research Articles

Evaluating Alternative Transformation Pathways of Wood-Based Bioeconomy: Application of an Input–Output Model

The wood-based bioeconomy is one of the main pillars of sustainable transformation and decarbonisation of the economy, as it promotes the use of renewable resources, reduces reliance on fossil fuels, and supports the development of eco-friendly industries. The paper provides quantitative insight into the potential impacts of transformation of the wood-based bioeconomy in Slovenia, a small, export-oriented economy with ample, but sub-optimally used, forest resources. The analysis uses an input–output (I-O) model to estimate I-O multipliers of sectors representing the wood-based bioeconomy and to capture their direct and indirect impacts on the Slovenian economy. The baseline performance of the wood bioeconomy sectors and their potential to induce economic activity (Scenario A) are estimated with an adjusted I-O model (based on the national I-O table for 2015), in which hybrid sectors are divided into conventional and bio-based segments. Alternative transformation pathways are translated into four additional scenarios. While Scenario B foresees progress in efficiency and integration within the existing structure of transactions, Scenario C (increased energy use of wood biomass) and Scenarios D/D+ (technology- and capital-intensive restructuring) represent two extreme restructuring pathways of the sector. The results of scenario analysis with the I-O model show that the changes in the input requirement structure of the Slovenian wood-based bioeconomy could result in up to a 17% increase in total output, up to a 20% increase in the number of employees, and up to a 16% increase in incomes, all of them attributed to the most optimistic scenario (D+). The results of the study provide quantified assessments underpinning strategic planning for the wood-based bioeconomy, both in the industry and public policy spheres.

Development of an input-output CGE model for collaborative management of water pollution reduction and carbon mitigation

Achieving the dual goals of improving water quality and reducing carbon emissions requires a systematic study of the combined effects of economic and environmental policies on industrial systems. A CGE-IWCR model is developed to examine the long-term evolution of industrial economic and environmental responses in the Yangtze River Economic Belt (YREB) under varying levels of carbon and water pollution taxes. The CGE-IWCE model offers several advantages: i) under the dual tax (carbon and water pollution tax) interventions, it can effectively forecast industrial CO2 and water pollution emissions driven by both macro and local factors from 2025 to 2060; ii) it quantitatively captures the interactions between various policy interventions, thereby providing guidance for comprehensive regional policy formulation. It is discovered that in the double-tax scenario, various combinations of carbon and water pollution taxes significantly impact direct carbon emissions and direct water pollution emissions, and from local consumption, imports, and exports in industrial sectors. Among them, the dual pressure of high carbon and water pollution taxes, which significantly raise production costs. In addition, increasing the carbon tax from 10 ¥/tonne to 50 ¥/tonne, the water pollution equivalent (WPE) reduction rate rises from 20.79% to 52.67% with the increase in the carbon tax from 2050 to 2060, when the water pollution tax is low. Between 2025 and 2060, the carbon tax plays a significant role in influencing the total industrial output change rate in YREB. from 2025 to 2060, the water pollution tax will play a more significant role in CO2 reduction in YREB’s industrial sector. The results will offer decision-making support for water pollution reduction and carbon mitigation in the YREB’s industrial sector, quantitatively identify the interactions between the dual taxes, and provide new insights for analyzing the impacts of pollution reduction and carbon mitigation policies.

Analysis of the Forward and Backward Linkages of Agriculture, Forestry and Fisheries Sectors in Central Java Province

This study aims to analyze the linkages forward and backward sectors of Agriculture, Forestry and fisheries in the economic development of Central Java province. This study uses data Input Output table of Central Java in 2016. The method of data analysis used in this study is Input-Output Analysis. The results showed that the subsector in the Agriculture, Forestry and fisheries sector that has the highest value of direct and indirect backward linkage is the livestock subsector. This means that the livestock subsector means that each increase in final demand in the livestock subsector by 1 unit will increase the output of other economic sectors that are used directly or indirectly as inputs to the livestock subsector by 1.5330 units. On the other hand, the value of direct and indirect future linkages in the agricultural, forestry and fisheries sectors is the highest, namely the food crop subsector with a value of 1.3903. This means that every increase in output in all sectors of the economy by 1 unit will increase the output of the food crop subsector by 1.3903 which is used directly as input for all sectors of the economy. Keywords: Backward Linkage, Forward Linkage, Economic Development, Agriculture, Forestry And Fisheries Sectors

Provincial inequalities in life cycle carbon dioxide emissions and air pollutants from electric vehicles in China

It is still unclear whether emissions reductions from electric vehicles can be achieved across different regions from a lifecycle perspective. Here we use the life cycle assessment model and Quasi Input-Output model to evaluate the carbon dioxide emissions and air pollutants of internal combustion engine vehicles, plug-in hybrid electric vehicles, and battery electric vehicles in different provinces of China, with the provincial electricity consumption data and the sales data of electric vehicles. We find that battery electric vehicles have achieved 11.8% and 1.1% reduction in carbon dioxide and nitrogen oxide emissions, respectively, compared to internal combustion engine vehicles. In contrast, the emissions of sulfur dioxide and particulate matter 2.5 increased by 10% and 20%, respectively. Due to the coal-based power generation structure and the cold weather, the emission intensity of battery electric vehicles in most northern provinces is higher than that in southern provinces. From 2020 to 2030, improving technological progress and optimizing electricity mix will greatly assist in achieving emissions reduction. The results can help policy-makers better understand the emission characteristics and reasonably plan future emission reduction strategies in transportation.

Research into Energy-Saving Control Strategies of a Bulldozer Driven by a Torque Converter Based on the Minimum Fuel Consumption Rate of the Whole Machine

In order to address the issue of poor fuel efficiency in hydraulic bulldozers during operation, this paper proposes a speed–load control strategy aimed at minimizing the overall fuel consumption rate of the machine. First, input–output models of the engine, hydraulic torque converter and the entire vehicle were established. Then, based on the bulldozer’s output power and fuel consumption rate, a performance metric for the overall fuel consumption rate was proposed to reflect the machine’s working efficiency. The characteristics of the overall fuel consumption rate were analyzed under different load, speed, and gear conditions. Next, a work point optimization control strategy was proposed for the whole machine energy-saving mode and constant power mode, aiming to minimize the overall fuel consumption rate while using the load, speed, and gear as control variables. To verify the feasibility of the work point optimization control strategy, simulations were conducted for both the energy-saving and constant power modes. The simulation results showed that in the whole machine energy-saving mode, this control strategy resulted in the lowest fuel consumption compared to constant speed control strategies at 1500, 1700, and 2000 rpm, with significant reductions in most cases. In the constant power mode, a comparison between four optimal operating points and the whole machine energy-saving mode revealed that while the former had slightly higher fuel consumption, it ensured a stable power output. Finally, experimental testing demonstrated that the proposed control strategy reduced the overall fuel consumption rate by 12.5% compared to the constant speed mode, verifying the effectiveness of the strategy. The study concludes that the energy-saving control strategy, through the coordinated switching between the two modes in complex operating conditions, not only ensures a stable power output for the bulldozer but also significantly improves fuel efficiency, providing an important reference for optimizing the efficiency of transmission systems in construction machines.

Energy input-output analysis cost for tomato production in Turkey

Energy input-output analysis cost for tomato production in Turkey

Suppression Limit Cycles in 2x2 Nonlinear Systems with Memory Type Nonlinearities

For several decades, the importance and weight-age of prediction of nonlinear self-sustained oscillations or Limit Cycles (LC) and their quenching by signal stabilization have been discussed, which is confined to Single Input and Single Output (SISO) systems. However, for the last five to six decades, the analysis of 2x2 Multi Input and Multi Output (MIMO) Nonlinear Systems gained importance in which a lot of literature available. In recent days’ people have started discussing suppression of LC which limits the performance of most of the physical systems in the world. It is a formidable task to suppress the limit cycles for 2x2 systems with memory type nonlinearity in particular. Backlash is one of the nonlinearities commonly occurring in physical systems that limit the performance of speed and position control in robotics, automation industry and other occasions like Load Frequency Control (LFC) in multi area power systems. The feasibility of suppression of such nonlinear self-oscillations has been explored in case of the memory type nonlinearities. Backlash is a common memory type nonlinearity which is an inherent Characteristic of a Governor, used for usual load frequency control of an inter-connected power system and elsewhere. Suppression LC using pole placement technique through arbitrary selection and optimal selection of feedback Gain Matrix K with complete state controllability condition and Riccati Equation respectively and is done through state feedback. The Governing equation is d/dt [X(t)] =(A-BK) X: which facilitates the determination of feedback gain matrix K for close loop Poles / Eigen values placement where the limit cycles are suppressed/eliminated in the general multi variable systems. The complexity involved in implicit non-memory type or memory type nonlinearities, it is extremely difficult to formulate the problem for 2x2 systems. Under this circumstance, the harmonic linearization/harmonic balance reduces the complexity considerably. Still the analytical expressions are so complex which loses the insight into the problem particularly for memory type nonlinearity in 2x2 system and the method is made further simpler assuming a 2x2 system exhibits the LC predominately at a single frequency. Hence in the proposed work an alternative attempt has been made to develop a graphical method for the prediction of Limit Cycling Oscillations in 2x2 memory type Nonlinear systems which not only reduces the complexity of formulations but also facilitates clear insight into the problem and its solution. The present techniques are well illustrated with an example and validated / substantiated by digital simulation (developed program using MATLAB codes) and use of SIMULINK Tool Box of MATLAB software. The present work has the brighter future scope of: Adapting the Techniques like Signal Stabilization and Suppression LC for 3x3 or higher dimensional nonlinear systems through an exhaustive analysis. Analytical/Mathematical methods may also be developed for signal stabilization using both deterministic and random signals based on Dual Input Describing function (DIDF) and Random Input Describing Function (RIDF) respectively. The phenomena of Synchronization and De-synchronization can be observed/identified analytically using Incremental Input Describing Function (IDF), which can also be validated by digital simulations.

Quenching and Suppression of Limit Cycles in 3x3 Nonlinear Systems

For several decades, the importance and weight-age of prediction of nonlinear self-sustained oscillations or Limit Cycles (LC) and their quenching by signal stabilization have been discussed which is confined to Single Input and Single Output (SISO) system. However, for the last five to six decades, the analysis of 2x2 Multi Input and Multi Output (MIMO) Nonlinear Systems gained importance in which a lot of literature available. In recent days few literatures are available which addresses the exhibition of LC and their quenching/suppression in 3x3 MIMO Nonlinear systems. Poor performances in many cases like Load Frequency Control (LFC) in multi area power system, speed and position control in robotics, automation industry and other occasions have been observed which draws attention of Researchers. The complexity involved, in implicit nonmemory type and memory type nonlinearities, it is extremely difficult to formulate the problem in particular for 3x3 systems. Under this circumstance, the harmonic linearization/ harmonic balance reduces the complexity considerably. Still the analytical expressions are so complex which loses the insight into the problem particularly for memory type nonlinearity in 3x3 system. Hence in the present work a novel graphical method has been developed for prediction of limit cycling oscillations in a 3x3 nonlinear system. The quenching of such LC using signal stabilization technique using deterministic (Sinusoidal) and random (Gaussian) signals has been explored. Suppression LC using pole placement technique through arbitrary selection and optimal selection of feedback Gain Matrix K with complete state controllability condition and Riccati Equation respectively. The method is made further simpler assuming a 3x3 system exhibits the LC predominantly at a single frequency, which facilitates clear insight into the problem and its solution. The proposed techniques are well illustrated with example and validated/substantiated by digital simulation (a developed program using MATLAB codes) and use of SIMULINK Tool Box of MATLAB software. The Signal stabilization with Random (Gaussian) Signals and Suppression LC with optimal selection of state feedback matrix K using Riccati Equation for 3x3 nonlinear systems have never been discussed elsewhere and hence it claims originality and novelty. The present work has the brighter future scope of: i. Adapting the Techniques like Signal Stabilization and Suppression LC for 3x3 or higher dimensional nonlinear systems through an exhaustive analysis. ii. Analytical/Mathematical method may also be developed for signal stabilization using both deterministic and random signals based on Dual Input Describing function (DIDF) and Random Input Describing Function (RIDF) respectively. iii. The phenomena of Synchronization and De-synchronization can be observed/identified analytically using Incremental Input Describing Function (IDF), which can also be validated by digital simulations.

Analysis of Ghana—Nigeria Export Trade: Linkages, Impacts and Policy Implications

This study hypothesised that the Ghana-Nigeria export trade has a significant impact on the economy of Ghana. Time series on variables of interest were obtained from the World Development Indicator (WDI) of the World Bank and the Ghana Statistical Service (GSS). The 2018 input-output table for Ghana was obtained from the social accounting matrix of the Organisation for Economic Co-operation and Development (OECD). The data were analysed using descriptive statistics, input-output analysis, and the autoregressive distributed lag (ARDL) bound testing approach. Through input-output analysis, this study found significant total impacts (151.585 to 190.139 million Ghana Cedis) in 2017 and 2018, centred around export commodities such as food and live animals, beverages, and tobacco. The expanding field of influence (214.373 to 268.898) and the ratio of impacts to Ghana's GDP (0.058% to 0.062%, then 0.044%) substantiate their pronounced role in the economy. The ARDL model showed that a percent increase in Ghana-Nigeria exports led to a 0.1017% increase in Ghana’s real GDP on the log-run. The speed of adjustment was 42.18% in each period. By providing support to exporters, both countries can leverage their comparative advantages, strengthen economic ties, and foster sustained growth through increased export trade.

Analyzing the Impacts of Inter-Provincial Trade on the Quantitative and Spatial Characteristics of Six Embodied Air Pollutants in China Through Multi-Scenario Simulation

Inter-provincial trade is accompanied by the transfer of embodied pollution emissions, leading to emissions leakage, thereby hindering the sustainable development of society. Therefore, it is imperative to analyze the characteristics of embodied pollutant emission and spatial transfer driven by inter-provincial trade. In this study, the quantitative and spatial characteristics of the six main embodied pollutants (i.e., SO2, NOX, CO, VOC, PM2.5, and PM10) were analyzed by a hypothetical extraction method (HEM) and complex network analysis (CNA) under an input–output analysis (IOA) framework. Then, the row arrange series (RAS) method was employed to simulate the impacts of varying levels of trade intensity, economic growth rate, and technological progress on embodied pollutants and spatial-transfer characteristics. The major findings are as follows: (i) the increase in inter-provincial trade led to a corresponding rise in embodied pollutant emissions due to the relocation of production activities towards provinces with higher emission intensity. Excessive responsibility was assumed by provinces such as Shanxi and Hebei, engaging in production outsourcing for reducing pollutants. (ii) The macro direction of pollutant transfer paths was from the resource-rich northern and central provinces towards the trade-developed southern provinces. Sectors in the transfer path, such as the industry sectors of Shanxi, Guangdong, Henan, and the transport sector of Henan, exhibited high centrality and dominated pollutant transfer activities in the network. (iii) The industry sector, characterized by substantial energy consumption, was the predominant emitter of all pollutant production-based emissions, accounting for more than 40% of total emissions. This study is conducive to analyzing the impacts of inter-provincial trade on embodied pollutant emissions and developing emissions reduction policies considering equitable allocation of emissions responsibilities from both production and consumption perspectives.

Analysis of Sustainable Efficiency of Freight Transport in Major European Economies: An Integrated Multi-Region Input-Output and DEA Approach

This paper integrates the multi-region input-output model (MRIO) and data envelopment analysis (DEA) methods to analyze the freight transport efficiency in Europe. Social, economic, and environmental influences were combined into a sustainable efficiency rating of the freight transport sector of Germany, France, Italy, Spain, and the Netherlands. First, the freight transport sector's carbon footprint (CFP) was quantified using the MRIO model. The lifecycle-based CFP emissions of freight transport activities were assessed using a dataset from 2000 to 2018. Nineteen stochastic model-based MRIO lifecycle assessments were built for each country. Secondly, sixty instances of DEA models were created using a linear program for each mode in the selected countries. Thirdly, the sustainable efficiency scores were determined for each freight transport mode in each country over four periods: 2000–2004, 2005–2009, 2010–2014, and 2015–2018. The results illustrate that the sustainable efficiency score of inland, water, and air transport modes ranged from 0.38 to 1.

Stability and receptivity analyses of the heated flat-plate boundary layer with variable viscosity

This article presents the modal, non-modal, and resolvent analyses of the boundary layer developed over a heated flat-plate with temperature-dependent viscosity using linear stability theory. The governing equations are derived in the normal velocity–vorticity form by imposing small infinitesimal disturbances on the base flow with the Oberbeck-Boussinesq (OB) approximation. The spectral method is employed to discretize the governing stability equations in the wall-normal direction. The base flow comes from the similarity solution using R-K4th order method along with shooting techniques. The effects of viscosity stratification, inertia, shearing, and buoyancy on the stability of the boundary layer are investigated by varying the sensitivity parameter (ϵ), Reynolds (Re), Prandtl (Pr), and Richardson numbers (Ri). The modal analysis shows the time-asymptotic behavior of the disturbances and onset of instability is mainly caused by amplification of Tollmien–Schlichting (T-S) waves similar to as observe in the traditional Blasius case. The modal stability increases with increase in sensitivity parameter and stabilizing effects become more pronounced for liquid than gas. However, the thermal effects lead to destabilize the flow and strong destabilizing effects produced for higher Prandtl number. On the other hand, non-modal analysis displays an early transient growth of disturbances and an existence of these non-normality effects identified from the pseudospectra via. resolvent analysis. The non-modal growth increases with increase in ϵ even though T-S modes shift towards the damped region, which indicates the continuous modes in the eigenspectrum are more dominated than the discrete T-S modes due to the thermal effects. To clarify this qualitative change, a component-wise input–output analysis is performed to measure the receptivity to particular external disturbances. The results show the thermal energy of the disturbances is converted into kinetic energy due to thermal effects, resulting in strong receptivity amplification at the continuous mode due to the non-normality of the linear operator. Thus, the boundary layer under the influence of viscosity stratification, heating, and shearing effects is vulnerable to free-stream disturbances that could significantly affect bypass transition

Mathematical modeling of Ethiopia's energy demand by sectors and energy types, with forecasts for the next 30 years

Due to the scarcity of economic resources, it is vital to optimize everything so that the supply and demand lines intersect in an optimized quantity, with no shortage or surplus of a provided item or service. Energy supply contains both surplus and shortage, thus estimating the amount of projected energy demand is a key work that must be completed. The objective of this paper is generating a mathematical model based on the actual data for thirty years forecasting. To create a mathematical model using actual data from the last fifteen years, a model that can represent the past trend and be used for future forecasting. This study provides a general overview of Ethiopia's current energy requirement with different energy type as well as sector-specific energy demand and estimates for different economic growth scenarios up to 2052. This model was created using a linear regression polynomial fit through Origin graphic and analysis software, and an econometric model was also applied. GDP was utilized as an independent variable in the economic model to determine the trend of energy consumption. Another input-output model is also used for multilinear regression to evaluate the change of four variables, GDP, population growth, urbanization growth rate, and general inflation rate, which was quantitatively linked with total energy requirement using Weka software. The mathematical model developed through linear and multilinear regression has been validated by using a different assumption on GDP growth based on past growth rate as low, medium and high growth rate and using the mathematical formula to generate an energy demand trend that can be compared with the actual trend; as a result, all the mathematical model that are generated has been found to be valid for the purpose of the intended work. Based on the generated mathematical model and different GDP growth rate scenario as low, medium, business as usual (BAU) and high a future energy demand was forecasted up to 2052 for thirty years. The model's results can help energy planners ensure that the country's supply capacity keeps up with predicted energy demand growth.

The deindustrialization of the Brazilian economy: new evidences and the role of 1990s productive restructuring in this process

ABSTRACT The study brings new interpretations about the phenomenon of Brazilian deindustrialization. From a perspective that has been used recently, the Marxian analysis associated with the inter-regional input-output analysis, we sought to understand how manufacturing lost space in the dynamization of Brazil’s economy. Presenting a critique of the use of relative manufacturing employment as an indicator of deindustrialization and understanding deindustrialization as a loss of manufacturing participation in the process of reproduction of capital on an expanded scale in a national economy, the domestic and foreign components of manufacturing backward multipliers were analyzed. In line which the literature, the main results indeed point to the existence of deindustrialization in the period between 1995 and 2010. In turn, as a new element raised from the discussion of the results in light of current literature, the hypothesis raised was that the deindustrialization resulted from the productive restructuring process that occurred in Brazil from the 1990s onwards.

Estimating the dynamic economic impacts of oil supply disruptions on China: A case study of Malacca Strait block

The intensification of geopolitical conflicts necessitates formulating economically effective coping strategies, which requires scientifically understanding the economic losses caused by oil supply disruptions and their evolutionary patterns. However, current assessment results are mostly static and cover direct losses, which are inadequate to fully support the development of disaster prevention measures. Therefore, considering the interdependence across different sectors, we employed a Dynamic Inoperability Input-Output Model to assess the economic impacts of a 20% disruption in oil imports through the Malacca Strait, lasting for 90 days. The main findings are as follows: (1) 2% of a sector's inoperability caused by oil supply disruption will, on average, be transmitted to other sectors due to their interdependencies. (2) The disruption will result in total economic losses of 62.7 billion yuan for China, and 70% of the total economic losses were incurred within the first 30 days. (3) To cope with the adverse economic impacts of oil transport disruption in the Malacca Strait, the optimal range of China's strategic petroleum reserve is 47.91–169.55 million barrels.

Evaluating the robustness of the ARIO model for a local disaster: 2021 flooding in Germany

Abstract Given the interconnectedness of modern economies and the widespread adoption of just-in-time production methods, even minor disruptions caused by natural disasters can lead to substantial indirect economic impact. A substantial body of literature has explored this phenomenon, using input-output analysis, computable general equilibrium and agent-based models. However, these models (i) heavily rely on parameters and data that often lack empirical grounding or (ii) exhibit considerable uncertainty, making it challenging to assess their reliability. The ARIO model has been widely used in the literature and has provided theoretical foundation for several related models. Using the July 2021 floods in Germany as a case study, we assess the sensitivity of the results of this model by varying key parameters, as well as the multi-regional input-output tables (MRIOTs), which constitute its primary input data. To facilitate this analysis, we introduce a new, resource-efficient Python implementation of the ARIO model, enabling the execution of a large number of simulations. Our findings highlight the substantial impact of data source and parameter selection on model outcomes, especially so when post-disaster rebuilding is costly. To ensure the robustness of their results, future studies on indirect economic impacts should be careful about recovery dynamics, consider multiple scenarios and compare results using MRIOTs from various sources.

Extended material requirement planning (MRP) within a hybrid energy-enabled smart production system

A smart production system can be made energy-efficient using renewable energy and is considered to maintain the extended material requirement planning under a logistics system by using radio frequency identification. The tracking technology provides information about products with real-time notification. This study investigates renewable energy usage within a smart production system as renewable energy can contribute to Net Zero Emissions. The logistics framework involves an autonomation technology-based production system, optimum cash flow, logistics, and carbon emissions. Time is an essential influencer for material requirement planning. The model is solved with a Laplace integral transformation, where an associated matrix method is utilized by the input–output analysis. The theoretical concept is elaborated through an illustrative numerical example, where the energy consumption and corresponding net present values are evaluated. Numerical and graphical studies prove the effectiveness of the model for the use of renewable energy within for material planning under a reverse logistics system. The result reveals that efficient renewable energy consumption can save considerable costs and reduce the negative net present value of the system. It is found that skilled workers are worthy of a smart production system, not only in a qualitative aspect but also in an economic aspect.

Projected Economic Impact of Sediment Removal Programs at Priority Locations

Removing marine sediment is a Ministry of Marine Affairs and Fisheries program regulated by Government Regulation No. 26 of 2023. Five priority locations have been designated for the removal of marine sediment: Riau Islands Province, West Java Province, East Kalimantan Province, Central Java Province, and East Java Province. This study uses input-output analysis to measure and compare the economic impacts of sediment removal activities on the economies of these five provinces. The input-output table results show that the highest output multiplier for sediment removal activities is in East Kalimantan Province with a value of 1.48, while the lowest is in the Riau Islands Province with a value of 1.22. The highest income multiplier is also found in East Kalimantan Province with a value of 0.3710, followed by Riau Islands Province with a value of 0.3513. In East Kalimantan and Riau Islands, sediment removal is a key sector with strong forward linkages to other sectors but weak backward linkages. One strategy to enhance the economic impact of sediment removal is for the government to spend non-tax state revenue (PNBP) from this activity on key sectors in each province.

Promoting Sustainability: Collaborative Governance Pathways for Virtual Water Interactions and Environmental Emissions

This study explores the water consumption and greenhouse gas (GHG) emissions in the Yarkand River Basin, focusing on their dynamic interactions across industrial sectors. Utilizing environmental input–output analysis (IOA), the CROPWAT model, and life cycle assessment (LCA), we quantified the historical evolution of physical and virtual water cycles in relation to the water–carbon nexus. Our findings reveal that the planting industry, particularly the production of export-oriented, water-intensive crops like cotton, significantly contributes to both blue and green water consumption, exacerbating regional water scarcity. The persistent external market demand drives this over-extraction, further strained by the basin’s limited water retention capabilities. Although advancements have been made in reducing the per-unit water footprint of crops, total water consumption continues to rise due to agricultural expansion, intensifying pressure on blue water resources. Additionally, agricultural GHG emissions have surged, driven by increased electricity consumption, heavy fertilizer use, and escalating soil N2O emissions. In light of these challenges, our research underscores the critical need for integrated resource management strategies that align with sustainable development goals. By promoting efficient water allocation within the agricultural sector and diversifying crop structures downstream, we can enhance ecosystem resilience and reduce environmental degradation. Furthermore, the advancement of value-added agricultural processing and the implementation of innovative water conservation technologies are essential for fostering economic sustainability. These strategies not only mitigate the environmental impacts associated with agricultural practices but also strengthen the region’s adaptive capacity in the face of climate change and fluctuating market demands. Our findings contribute to the broader discourse on sustainable agricultural practices, emphasizing the interconnectedness of water management, climate resilience, and economic viability in arid regions.

Towards net-zero operating in neurosurgery

Background The climate crisis is one of the greatest threats to public health and surgery is a significant contributor to carbon emissions generated by the NHS. In this paper, we describe our experience of sustainable operating by using evidence-based methods to reduce our carbon footprint across three neurosurgical theatres during our ‘Green operating day’. Methods The Green operating day was run at a single site over a 12-hour operative day and included 10 neurosurgical cases. Following discussions with the theatre, anaesthetic and sustainability team, each operative case was reviewed and changes in the consumables, surgical instruments and utilities recorded. Carbon footprint was calculated using an environmentally extended input-output model for baseline and Green operating day. Qualitative data was collected on the participants of the Green operating day to assess attitudes and behaviours towards sustainability in neurosurgery. Results There was a total reduction of carbon emissions by 31%, equivalent to 1.04 tonnes CO2e. Reductions were seen across different aspects of surgery including anaesthetics, surgical instruments, waste and utilities. Discussion This study demonstrates the feasibility of carbon footprint reduction within neurosurgical theatres which was not associated with increases in operative duration or adverse patient outcomes. This study advocates for environmentally conscious decision making in neurosurgical procedures.