Input Output Research Articles

Dissecting household carbon and energy inequality: A decade's insight into regional disparities and urban-rural dynamics in China

Substantial inequalities exist in carbon and energy footprints across households in China, due to variations in income and consumption patterns. Examining these inequalities and their driving factors amid the rapid economic and energy transition is crucial for making informed climate policies. Here, we investigate the variations and driving factors of household carbon and energy inequality across urban and rural areas and ten income groups in China during 2007–2017 by employing the environmentally extended multiregional input–output approach and national household consumption survey data. The results reveal that urban regions tend to exhibit higher carbon and energy footprints, coupled with greater inequality, compared to rural regions. The carbon and energy inequalities in the less developed provinces are mainly attributed to urban-rural differences, whereas in the more developed provinces, intra-urban differences play a dominant role. The carbon and energy inequality declined in China during 2007–2017. We argue that the reduction in carbon and energy inequality in China can be attributed to footprints increases in rural regions, a trend that should be carefully considered in future low carbon development policy formulation.

Sustainable production and dissemination of genetically improved farmed tilapia seed in smallholder farming systems: A case study from Timor-Leste

CONTEXTTimor-Leste's National Aquaculture Development Strategy (NADS) 2012–30 aims to achieve an annual aquaculture production of 12,000 tons by 2030 to raise per capita fish consumption from 6.1 kg in 2010 to 15 kg. Genetically Improved Farmed Tilapia (GIFT) is identified as the most suitable species under Timor-Leste's existing farming systems and agro-ecological context. It is expected to account for two-thirds of total aquaculture production, requiring over 30 million fingerlings annually, necessitating a strong role of public and private sectors to develop a sustainable hatchery model for producing and supplying high quality GIFT fingerlings to fish farmers on a mass-scale. OBJECTIVEThe major objective was to conceptualize and apply a public-private partnership (PPP) model to produce and disseminate high-quality monosex GIFT seed by public and private hatcheries across Timor-Leste. METHODSModel GIFT hatcheries were established and monitored using a standard protocol developed by the R&D team. Data on total eggs, clutch size, incubation survival rate, swim-up fry, and monosex fry produced were recorded on a weekly basis. The performance of hatcheries was compared using descriptive statistics at a 5% level of significance. Economic analysis was conducted to compare production costs, gross profits, and net profits among hatcheries and nurseries. Enabling conditions and factors for effective PPP were identified. RESULTS AND CONCLUSIONSThe WorldFish, in collaboration with Timor-Leste's Ministry of Agriculture and Fisheries, upgraded an existing public hatchery in Gleno, Ermera, introduced GIFT in 2015, and developed it as a GIFT broodstock centre to supply quality brood fry to other hatcheries. To produce monosex seed on a mass-scale, four PPP model GIFT hatcheries were established between 2019 and 2023. Results from a public hatchery and two initially established PPP model hatcheries showed that the hatchery in Parlamento had significantly higher (P < 0.05) monthly production of eggs, swim-up fry, monosex fry, and clutch sizes. On average, over 177,000 monosex fry per month were produced by those three hatcheries. Local service providers (LSPs), self-employed youths who have been recruited and trained to facilitate input supply and output marketing, purchase GIFT fry from the hatcheries, nurse them to fingerling size, and supply them to farmers. To achieve high seed production and supply targets, scaling of PPP model hatcheries, nurseries, and LSPs along with effective training and technical back-up, favourable policies, institutional environments, and governance mechanisms, is necessary. SIGNIFICANCEThe PPP model GIFT hatcheries are scalable across inland farming systems in Timor-Leste and other countries with similar agro-ecological, socio-economic, and climatic contexts.

FAOSTAT Food Value Chain Domain implementation: Input Output modelling and analytical applications

The recent increasing attention to the economic and policy analysis of the food systems from international fora, public institutions and academia calls for the availability of information and data capable of informing about the interrelations across economic sectors and within value chains. The international policy agenda is pushing for a more effective application of measures at country and regional level in line with the recommendations of the 2030 Agenda and its Sustainable Development Goals, for which more systematic and integrated data about economic, social and environmental impacts of policies are requested. The Food Value Chain Domain recently published in FAOSTAT responds to this call. Its data and information shed light on the distribution of final domestic food expenditures across industries (Agriculture, Food Processing, Wholesale, Retail, Accommodations and Food Services) and primary factors (e.g.: Labour, Gross Operating Surplus) on the relative food value chain. The FAOSTAT Domain offers therefore robust and granular information on both the farm and the post-farm gate component of the Food Value Chain. The applied Global Food Dollar methodology, that FAO is contributing to upscale at global level, is based on Leontief decomposition approach on the Input-Output tables. Moreover, whenever the Input-Output table are not available, it is now possible to impute them from Supply-Use tables by applying a conversion methodology, developed by FAO in compliance with European (EUROSTAT), United Nations (UNSD) and international statistical standards as the System of National Accounts. This allows to extend the analysis to several African, Asian, and Latin American countries that produce on regular basis only Supply and Use Tables, and not Industry by industry Input Output Tables. The potential time and data coverage of the methodology is therefore significantly expanded. The aim of this paper is to describe the conceptual framework of the conversion methodology of Supply-Use Tables into Input-Output Tables of the Global Food Dollar methodology, and the potential implementation scope of these methodologies. Preliminary analytical findings of the applied methodologies are presented as well. The new methods and data presented in this paper, being based on data compliant with the International Statistical Standards, as the System of National Accounts, and therefore comparable across countries, associated to larger data availability, have the potential to effectively support food policies at international, regional and national level, as well as contribute to a decision making in line with the 2030 Agenda.

Energy use pattern in rose onion (&lt;i&gt;Allium&lt;/i&gt; &lt;i&gt;cepa &lt;/i&gt;L.) cultivation

A study was conducted to analyse the energy use pattern for cultivation and on farm processing of rose onion (Allium cepa L.). The energy auditing data was collected by stratified random sampling method using a face- to-face interaction at Sadali (Hobli), Sidlaghatta, Chikkaballapur, Karnataka. In this region, the energy utilized for different package of practices followed for rose onion cultivation by conventional practice are land preparation (5-tyne cultivator, 9-tyne cultivator and rotovator), sowing (broadcasting), thinning (manual), manure &amp; fertilizer application (manual), plant protection {weeding (manual and chemical spray), chemical spraying (battery operated sprayer)}, irrigation (micro-irrigation), harvesting (manual) and detopping (manual). The energy use pattern for the above-mentioned various package of practices were found to be 4,207.95±37.21, 664.66±17.68, 53.31±2.68, 22,522.92±385.07, 2,534.40±155.55, 14,980.51±229.49, 807.74±20.80 and 1,571.75±42.77 MJ ha-1, respectively. The input energy, output energy and energy ratio were calculated as 47,343.23±484.65, 38,131.12±462.48 MJ ha-1 and 0.81±0.01, respectively. The energy intensive operation identified was manure and fertilizer application (fertilizer 46.80%; men 0.77%) both indirect and direct energy sources, followed by irrigation (electricity 31.09%; men 0.55%), land preparation (diesel 8.33%) and pesticide application (pesticide 4.53%). It is concluded that the fertilizer, electricity and diesel utilised in rose onion cultivation needs to be optimally minimised through management practices.

FDT: Improving the transferability of adversarial examples with frequency domain transformation

The development of Deep Neural Networks (DNNs) has facilitated profound advancements in computer vision. While DNNs demonstrate substantial capabilities, their susceptibility to adversarial attacks can introduce significant errors and hinder their applicability in real-world scenarios. The characteristic of black-box attacks is that they rely on the input–output mapping of the model without accessing its parameters or gradients. Due to the disparities between the substitute and target models, black-box attacks typically exhibit reduced success rates. To address the challenges outlined previously, we propose the Frequency Domain Transformation (FDT) method that employs the Discrete Cosine Transform (DCT) for transforming the input image into the frequency domain. It innovatively applies a grid mask to generate adversarial samples within the frequency domain. This transformation diminishes the spatial correlation among the image pixels and offers a fresh perspective for enhancing the transferability of adversarial examples. Experiments on adversarial attacks using the ImageNet dataset reveal that adversarial examples, when created through methods that transform data in the frequency domain, show enhanced transferability compared to those produced by transformations in the spatial domain. In attacks on six classification models using the single-model approach, FDT achieved an average success rate of 82.9%, corresponding to a 14.4% improvement over the spectrum simulation attack (SSA) method. For ensemble-model attacks with momentum-based techniques, the average success rate using the proposed strategy was 94.4%, corresponding to a 5.7% improvement over the SSA method.

Differential effects of stimulation frequency on isometric and concentric isotonic contractile function in human quadriceps.

Electrically evoked contractions are used to assess the relationship between frequency input and contractile output to characterize inherent muscle function, and these have been done mostly with isometric contractions (i.e., no joint rotation). The purpose was to compare the electrically stimulated frequency and contractile function relationship during isometric (i.e., torque) with isotonic (i.e., concentric torque, angular velocity, and mechanical power) contractions. The knee extensors of 16 (5 female) young recreationally active participants were stimulated (~1-2.5s) at 14 frequencies from 1-100 Hz. This was done during four conditions, repeated on separate days, which were: isometric; and isotonic at loads of 0, 7.5 and 15% isometric maximal voluntary contraction (MVC). Comparisons across contractile parameters were made as a % of 100 Hz. Independent of the load, the mechanical power-frequency relationship was rightward shifted compared with isometric torque-, concentric torque- and velocity-frequency relationships (all p≤0.04). With increasing load (0-15% MVC) the isotonic concentric torque-frequency relationship was shifted leftward systematically from 15-30 Hz (all p≤0.04). Conversely, the same changes in load caused a rightward shift in the velocity-frequency relationship from 1-40 Hz (all p≤0.03). Velocity was leftward shifted of concentric torque in the unloaded isotonic condition from 10-25 Hz (all p≤0.03), but concentric torque was leftward shifted of velocity at 15% MVC isotonic condition from 10-50 Hz (all p≤0.03). Therefore, isometric torque is not a surrogate to evaluate dynamic contractile function. Interpretations of evoked contractile function differ depending on contraction type, load, and frequency which should be considered relative to the specific task.

Combining simulation and experimental data via surrogate modelling of continuum dislocation dynamics simulations

Several computational models have been introduced in recent years to yield comprehensive insights into microstructural evolution analyses. However, the identification of the correct input parameters to a simulation that corresponds to a certain experimental result is a major challenge on this length scale. To complement simulation results with experimental data (and vice versa) is not trivial since, e.g. simulation model parameters might lack a physical understanding or uncertainties in the experimental data are neglected. Computational costs are another challenge mesoscale models always have to face, so comprehensive parameter studies can be costly. In this paper, we introduce a surrogate model to circumvent continuum dislocation dynamics simulation by a data-driven linkage between well-defined input parameters and output data and vice versa. We present meaningful results for a forward surrogate formulation that predicts simulation output based on the input parameter space, as well as for the inverse approach that derives the input parameter space based on simulation as well as experimental output quantities. This enables, e.g. a direct derivation of the input parameter space of a continuum dislocation dynamics simulation based on experimentally provided stress–strain data.

Estimating the uncertainty of the greenhouse gas emission accounts in global multi-regional input–output analysis

Abstract. Global multi-regional input–output (GMRIO) analysis is the standard tool to calculate consumption-based carbon accounts at the macro level. Recent inter-database comparisons have exposed discrepancies in GMRIO-based results, pinpointing greenhouse gas (GHG) emission accounts as the primary source of variation. A few studies have analysed the robustness of GHG emission accounts, using Monte Carlo simulations to understand how uncertainty from raw data propagates to the final GHG emission accounts. However, these studies often make simplistic assumptions about raw data uncertainty and ignore correlations between disaggregated variables. Here, we compile GHG emission accounts for the year 2015 according to the resolution of EXIOBASE V3, covering CO2, CH4 and N2O emissions. We propagate uncertainty from the raw data, i.e. the United Nations Framework Convention on Climate Change (UNFCCC) and EDGAR inventories, to the GHG emission accounts and then further to the GHG footprints. We address both limitations from previous studies. First, instead of making simplistic assumptions, we utilise authoritative raw data uncertainty estimates from the National Inventory Reports (NIRs) submitted to the UNFCCC and a recent study on uncertainty of the EDGAR emission inventory. Second, we account for inherent correlations due to data disaggregation by sampling from a Dirichlet distribution. Our results show a median coefficient of variation (CV) for GHG emission accounts at the country level of 4 % for CO2, 12 % for CH4 and 33 % for N2O. For CO2, smaller economies with significant international aviation or shipping sectors show CVs as high as 94 %, as seen in Malta. At the sector level, uncertainties are higher, with median CVs of 94 % for CO2, 100 % for CH4 and 113 % for N2O. Overall, uncertainty decreases when propagated from GHG emission accounts to GHG footprints, likely due to the cancelling-out effects caused by the distribution of emissions and their uncertainties across global supply chains. Our GHG emission accounts generally align with official EXIOBASE emission accounts and OECD data at the country level, though discrepancies at the sectoral level give cause for further examination. We provide our GHG emission accounts with associated uncertainties and correlations at https://doi.org/10.5281/zenodo.10041196 (Schulte et al., 2023) to complement the official EXIOBASE emission accounts for users interested in estimating the uncertainties of their results.

Impacts of forest plantation programs on sectoral CO2 emissions regionally in New Zealand

Investing in projects that support environmental benefits, such as tree harvesting, has the potential to reduce air pollution levels in the atmosphere in the future. However, this kind of investment may increase the current level of emissions. Therefore, it is necessary to estimate how much the policy affects the current level of CO2 emissions. This makes sure the policy doesn’t increase the level of CO2 emissions. This study aims to analyze the effect of the One Billion Trees program on CO2 emissions in New Zealand by employing the 2020 input–output table analysis. This investigation examines the direct and indirect effects of policy on both the demand and supply sides across six regions of New Zealand. The results of this study for the first year of plantation suggest that the policy increases the level of CO2 emissions in all regions, especially in the Waikato region. The direct and indirect impact of the policy leads to 64 kt of CO2 emissions on the demand side and 270 kt of CO2 emissions on the supply side. These lead to 0.19 and 0.74% of total CO2 emissions being attributed to investment shocks. Continuing the policy is recommended, as it has a low effect on CO2 emissions. However, it is crucial to prioritize the use of low-carbon machinery that uses fossil fuels during the plantation process.

Machine learning-enabled characterization of concrete mechanical strength through correlation of flexural and torsional resonance frequencies

In this study, an assessment of concrete compressive strength was conducted using an impulse excitation data-driven machine learning (ML) framework. The model was constructed upon a deep neural network and aided by the backpropagation method, ensuring a precise training process. In contrast to prior research, which mainly focused on mixture components, a meaningful relationship between physical parameters—resonant frequencies and elastic moduli—and compressive strength was established by our ML model. Remarkable performance was demonstrated, with a root mean square error value of 2.8MPa and a determination factor of 0.97. Through Pearson analysis, correlations between input features and output targets, ranging from −0.29 to 0.90, were revealed. Notably, the strongest correlations with compressive strength were found in Young's and shear moduli, derived from flexural and torsional frequencies, highlighting the pivotal role of dynamic elastic response in concrete's mechanical behavior. Furthermore, the findings indicated slight prediction deviations in cases involving samples with a high Poisson's ratio. This work illuminates the potential for accurate compressive strength prediction by leveraging concrete's dynamic response, particularly flexural and torsional modes, thereby opening avenues for research into concrete compressive strength without direct consideration of sample ingredients.

Robust Output Feedback Stabilization and Tracking for an Uncertain Nonholonomic Systems with Application to a Mobile Robot.

This paper presents a novel robust output feedback control that simultaneously performs both stabilization and trajectory tracking for a class of underactuated nonholonomic systems despite model uncertainties, external disturbance, and the absence of velocity measurement. To solve this challenging problem, a generalized normal form has been successfully created by employing an input-output feedback linearization approach and a change in coordinates (diffeomorphism). This research mainly focuses on the stabilization problem of nonholonomic systems that can be transformed to a normal form and pose several challenges, including (i) a nontriangular normal form, (ii) the internal dynamics of the system are non-affine in control, and (iii) the zero dynamics of the system are not in minimum phase. The proposed scheme utilizes combined backstepping and sliding mode control (SMC) techniques. Furthermore, the full-order high gain observer (HGO) has been developed to estimate the derivative of output functions and internal dynamics. Then, full-order HGO and the backstepping SMC have been integrated to synthesize a robust output feedback controller. A differential-drive type (2,0) the wheeled mobile robot has been considered as an example to support the theoretical results. The simulation results demonstrate that the backstepping SMC exhibits robustness against bounded uncertainties.

Scaled graphs for reset control system analysis

Scaled graphs allow for graphical analysis of nonlinear systems, but are generally difficult to compute. The aim of this paper is to develop a method for approximating the scaled graph of reset controllers. A key ingredient in our approach is the generalized Kalman-Yakubovich-Popov lemma to determine input specific input–output properties of a reset controller in the time domain. By combining the obtained time domain properties to cover the full input space, an over-approximation of the scaled graph is constructed. Using this approximation, we establish a feedback interconnection result and provide connections to classical input–output analysis frameworks. Several examples show the relevance of the results for the analysis and design of reset control systems.

Randomized compression of rank-structured matrices accelerated with graph coloring

A randomized algorithm for computing a data sparse representation of a given rank-structured matrix A (a.k.a. an H-matrix) is presented. The algorithm draws on the randomized singular value decomposition (RSVD), and operates under the assumption that methods for rapidly applying A and A∗ to vectors are available. The algorithm uses graph coloring algorithms to analyze the hierarchical tree that defines the rank structure to generate a tailored probability distribution from which to draw the random test matrices. The matrix is then applied to the test matrices, and in a final step the matrix itself is reconstructed by the observed input–output pairs. The method presented is an evolution of the “peeling algorithm” of Lin et al. (2011). For the case of uniform trees, the new method substantially reduces the pre-factor of the original peeling algorithm. More significantly, the new technique leads to dramatic acceleration for many non-uniform trees since it constructs test matrices that are optimized for a given tree. The algorithm is particularly effective for kernel matrices involving a set of points restricted to a lower dimensional object than the ambient space, such as a boundary integral equation defined on a surface in three dimensions.

Trade and decoupling of fossil fuel use embedded in EU consumption

The EU is frequently recognised as a frontrunner in tackling climate change; however, this assessment primarily relies on production-based evaluations, overlooking the significant role of imports. We conduct a detailed consumption-based analysis of how EU consumption drives global fossil fuel use, combining input–output with structural decomposition analysis and the subsystem approach. We find that the embedded EU fossil fuel footprint has experienced a notable decline between 2000 and 2014, but at rates incompatible with 1.5 °C. We identify trade patterns to be an important upward driver of the EU footprint through intermediate production, also thwarting the impact of the energy transition with effects from changes outside the EU lagging within EU developments. Addressing these outsourcing patterns to more fossil fuel intense production could reduce the EU footprint by almost 20%. We find that more than 50% of fossil fuels embedded in imports are linked to indirect imports. Thus, we argue for the EU Carbon Border Adjustment Mechanism to include indirect imports, particularly of electricity. Yet, given the problematic role of growth, even energy transition efforts along the global supply chain will likely need to be complemented by demand side measures, potentially entailing post-growth pathways.

Green Marketing and the Path to Realizing Local Sustainable Development—Joint Dynamic Analysis of Data Envelopment Analysis (DEA) and Fuzzy Set Qualitative Comparative Analysis (fsQCA) Based on China’s Provincial Panel Data

Understanding the mechanisms by which the combination of green marketing components fosters local sustainable development is crucial for global regions in achieving the United Nations’ sustainable development goals. Utilizing panel data from China’s provinces from 2011 to 2022, this study employs the DEA model to assess both the static and dynamic efficiencies of sustainable development across China’s 31 provinces. Subsequently, drawing on the 6P theory of marketing element combination, this study selects human input, capital input, energy input, technological output, economic output, and ecological output as causal variables, with the local sustainable development index serving as the outcome variable. By integrating the fsQCA method, the study identifies four distinct configurations: a “single input–multiple output” model, a “multiple input–single output” model, an “input–output” linkage model, and an “input-driven” model. This conclusion can enhance the comprehension of the dynamics through which various combinations of green marketing components contribute to local sustainable development from a regional macroeconomic perspective, offering a theoretical foundation for achieving sustainable development globally.

Equity Incentive, R&amp;D Input and Innovation Output: Based on the Analysis of Small and Medium-sized Enterprises

Under the influence of the general environment, many small and medium-sized enterprises are facing threats. However, some innovative enterprises continue to grow with innovation. This paper studies the relevant data of listed companies on the Growth Enterprise Market (GEM) from 2016 to 2021, aiming at exploring the relationship among equity incentives, research and development inputs and innovation outputs of small and medium-sized enterprises on the GEM. The research found that both equity incentive and R&amp;D investment will bring positive impact on innovation output, and R&amp;D expenditure plays an intermediary role between equity incentive and innovation results. It also inspires enterprises to alleviate the principal-agent problem through equity incentive in practice, to promote enterprise innovation and to promote the sustainable development of enterprises.

The temporal variation of CH4 emissions embodied in Chinese supply chains, 2000–2020

Although the issue of embodied pollutants in China’s supply chains has garnered increasing attention, the dynamic changes occurring within them are unclear. Several existing studies analyze one-year or short-term data in supply chain. China’s overall CH4 emissions have risen from 41.1 Tg in 2000 to 60 Tg in 2020, so conducting long-term analyses can yield a deeper understanding of the dynamic changes across the entire supply chain from production to consumption. This study uses the environmentally extended input–output analysis (EEIOA) and structural path analysis (SPA) methods to investigate the dynamic variation of China’s embodied CH4 emissions in 20 industry sectors from 2000 to 2020, aiming to determine the key supply chain and key sectors. The results reveal that from the final demand perspective, consumption, investment and export drove 52.1%, 32%, and 15.9% of embodied CH4 emissions in 2020. The sector with the highest embodied CH4 emissions has changed from “Agriculture” in 2000 to “Construction” in 2010 to “Other service and activities” in 2020. The top listed supply chain path of embodied CH4 emissions has also evolved (starting from production to consumption) from “Agriculture → Rural consumption” in 2000 to “Agriculture → Food and tobacco → Urban consumption” in 2010 to “Agriculture → Urban consumption” in 2020. Notably, the high-ranked path, “Agriculture → Food and tobacco → Rural consumption”, shows that the embodied CH4 emission flowing between agriculture and the food industry cannot be ignored. The supply chain path “Coal Mining → Nonmetal Mineral Products → Construction → Capital Formation” has risen from 17th in 2000 to 3rd in 2020. Thus, it is necessary to control CH4 emissions from sectors upstream, which are predominantly influenced by the construction industry, and a coordinated effort between sectors is also required to effectively reduce emissions. By 2020, the CH4 emissions driven by urban consumption were 3.1 times that of rural consumption. This study provides a comprehensive analysis of China's supply chain over the past two decades. In particular, it suggests policy interventions by controlling critical supply chain paths and key sectors associated with embodied CH4 emission, thereby facilitating the coordinated reduction of anthropogenic CH4 emissions.

Effect of the correlated color temperature of a phosphor-converted LED on indoor visible light communication

Correlated color temperature (CCT) is an important parameter of the phosphor-converted LED (PC-LED); international standards specify its range in different scenarios. However, little has been done to investigate the effect of CCT on the performance of visible light communication (VLC). In this paper, we propose an analytical model for the transmission performance of an LED VLC system based on the dual components of blue and yellow light from the luminescence mechanism of the PC-LED. On this basis, the effects of CCT on the PC-LED’s input current and output optical power between the dual components are analyzed. The simulation results show that the increase of CCT expands the modulation bandwidth of the PC-LED, and the trend of the BER with CCT is also related to the transmission data rate. In addition, the maximum data rate of the PC-LED with CCT from 2700 K to 6500 K under a certain transmission distance is also simulated in this paper.

Household Consumption in the Mirror of Input–Output Tables

Household Consumption in the Mirror of Input–Output Tables

Data-driven identification and comparison of full multivariable models for propofol–remifentanil induced general anesthesia

In this paper, we present results with clinical data to enable a 2x2 input–output multivariable patient model for hypnosis and analgesia. Nonlinear multi-drug interaction models are identified from data recorded from 70 patients undergoing surgery during total intravenous anesthesia (TIVA) with several medical monitors for variables such as Bispectral Index, Nociception level (Medasense), skin conductance (Medstorm) and advanced spectral analysis conductance (AnspecPro). Bispectral index measures the depth of hypnosis (lack of consciousness), while nociception related indices from Medasense, Medstorm, and AnspecPro devices measure levels related to analgesia (lack of reaction to noxious stimuli). A comparison is given among three response surface model (RSM) structures – Minto, Greco, and Reduced Greco – for hypnotic and analgesic states during Propofol–Remifentanil interaction. The identified models capture the pharmacodynamic properties of dose–effect concentrations of Propofol/Remifentanil while the pharmacokinetic part of the patient model is calculated from patient’s biometric values using Schnider/Minto (SM), and Eleveld/Eleveld (EE) models. In presence of strict clinical protocols delivering data under poor identifiability conditions, we propose two methods of identification: (i) based on steady-state gains, and (ii) using all available data which includes part of the dynamic transient. The model parameters are optimized with Genetic Algorithm based on a goodness of fit performance measure complemented with root mean square error. The results suggest that the EE model combination is advantageous for Bispectral index pharmacokinetic modeling at the cost of numerical complexity, therefore reducing the uncertainty left to be identified in the pharmacodynamic part of the patient model. By contrast, the SM model combination is less computationally demanding and provides some improvement in the RSM accuracy for nociception level indicators. The comparison of three devices for nociception levels evaluation suggests that clinical data captured with the Medasense monitor provides best fitted RSMs with the Reduced Greco RSM structure, despite having fewer parameters.