Early mobilization positively influences the outcome of critically ill patients, yet in clinical practice, the implementation is sometimes challenging. In this study, an adaptive robotic assistance system will be used for early mobilization in intensive care units. The study aims to evaluate the experience of the mobilizing professionals and the general feasibility of implementing robotic assistance for mobilization in intensive care as well as the effects on patient outcomes as a secondary outcome.n > 90) and episodic interviews (n > 36) before implementation, shortly after, and in routine. Patient outcomes such as duration of mechanical ventilation, loss of muscle mass, and physical activity will be measured and compared with a historical patient population. Approximately 30 patients will be included.The study is single-centric, prospective, and interventional and follows a longitudinal study design. To evaluate the feasibility of robotic-assisted early mobilization, the number of patients included, the number of performed VEM (very early mobilization) sessions, and the number and type of adverse events will be collected. The behavior and experience of mobilizing professionals will be evaluated using standardized observations (The study will provide information about patient outcomes, feasibility, and the experience of mobilizing professionals. It will show whether robotic systems can increase the early mobilization frequency of critically ill patients. Within ICU structures, early mobilization as therapy could become more of a focus. Effects on the mobilizing professionals such as increased motivation, physical relief, or stress will be evaluated. In addition, this study will focus on whether current structures allow following the recommendation of mobilizing patients twice a day for at least 20 min.NCT05071248. Date: 2021/10/21ClinicalTrials.gov,  Many studies have shown positive impacts of very early mobilization (VEM) on the functional and cognitive health –7 of intIt has also been described that VEM can prevent functional disorders , 10. RegHowever, optimal VEM therapy, i.e., mobilization starting within 72 h of ICU admission, should include daily mobilization sessions of at least 20 min, combining verticalization and gait-like leg movements. Due to the critical physical conditions of intensive care patients, VEM therapy can therefore only be carried out with an extraordinarily high level of personnel effort, especially if the patient is ventilated . Often, Currently, several devices on the market allow automated robotic early mobilization therapy. Compared to manual early mobilization, robotic support has the advantage that mobilization in bed can reduce the risk of falls for patients. In addition, the physical strain for mobilizing professionals is reduced as the robotic device takes over verticalization and leg movement. Some models verticalize and mobilize patients simultaneously. However, this requires a patient’s transfer from their bed to the training device and then back to the bed.The MobiStaR project (Mobilization of intensive care patients by a new standard in adaptive robotics) is based on the development model of complex interventions of the Medical Research Council (MRC) . In a cyThe early mobilization robot used in our study design is able to verticalize the patient in their bed without transfer. Additionally, it generates a movement of the legs while measuring and supporting the patient’s own movement. The device fulfills the requirements for mobilizing critically ill patients in an intensive care unit, maintains hygiene standards, and provides the best possible support for the patient’s own movement. However, the path towards a nursing robot that can be used in a standardized manner for all eligible, critically ill patients strongly depends on the environment, the processes, and the organizational procedures in which the robot is integrated. If it fulfills the requirements of sustaining the quality of care and significantly improves patient outcomes and their chances of recovery, thereby relieving the personnel, and is economically attractive, it simplifies the integration into an ICU. There is currently no adequate evidence for the benefit of the use of robotics in the early mobilization of ICU patients.Evidence-based data is currently lacking on whether the use of robotic-assisted early mobilization can improve patient outcomes, what the experience of users is like, and whether the organizational and structural implementation in the daily routine of an intensive care unit is possible.The aim of this interventional study is to determine if robotic-assisted early mobilization of critically ill patients is feasible and useable. In addition, it is intended to identify the effects of this form of VEM compared to conventional, manual VEM on the experience of the mobilizing profession and the outcomes of the patients.To achieve this purpose of the study, the following research questions will be examined in the context of (1) organizational feasibility, (2) evaluation of effects on patient outcomes, and (3) evaluation of the mobilizing professionals’ experience.The present study is a mixed-methods, single-centric, prospective intervention study with a comparison to actual standard therapy and takes place in anesthesiological intensive care units of a university hospital in southern Germany Table .Table 1SThe study population consists of patients undergoing elective surgical procedures and scheduled for postoperative treatment in the anesthesiological intensive care unit. Patients will be included in the prospective intervention study according to the following inclusion criteria: the surgical intervention and postoperative care and therapeutic treatment in the ICU are planned, and the preoperative patient consents in writing for the study. The expected duration of ventilation is more than 48 h. The patients are older than 18 years, their weight is between 45 and 135 kg, and their height is between 1.50 and 1.95 m. Exclusion criteria are chronic bedriddenness, a clinical frailty scale ≥ 7 [Patients within the historical comparison group will be retrospectively selected within the same criteria. If they met any of the exclusion criteria during their intensive care unit stay, they will not be included in the historical group. No matching of the interventional and historical groups is planned.The mobilizing professionals consist of physicians, nurses, and physiotherapists working in anesthesiological intensive care units and are regularly involved in mobilization. An employment contract at the Hospital is required for all professional groups/mobilizing professionals. Physicians, nurses, and physiotherapists will be included according to inclusion and exclusion criteria.2 and T3, all specialists should also be assigned to the anesthesiological intensive care units. Specialists will only be included if they have given consent to participate in the study.Nurses with advanced training in anesthesia and intensive care and/or nurses who have at least 3 years of professional experience in an intensive care unit will be included. In addition, these persons have an employment contract at Hospital. Similarly, specialists in leading positions in intensive care units with completed residency training meet the inclusion criteria. Additionally, physiotherapists with at least 3 years of professional experience in an intensive care unit will be included. For T2 and T3, specialists who are not assigned to the anesthesiological intensive care units according to the duty schedule are excluded.Persons that are members of the MobiStaR project team, have less than 3 years of professional experience as a nurse or specialist in an ICU, or are still in residency training will be excluded. Physiotherapists with less than 3 years of professional experience in intensive care units are also excluded. Individuals who are not employees of the Hospital are also excluded. In TIn order to test correlations using multiple-variate models (multiple linear regressions) with a statistical power of 80% on approximately 8 independent variables (IV) compared to the dependent variable (DV), an approximate total sample size of 50 patients is required. Thus, with an expected drop-out of 10%, 55 patients  should be included in the study. A sample size of 20 subjects is considered a lower limit with moderately strong associations between IVs and DV and inclusion of a maximum of 5 IVs, with alpha=5% and power=80% , 20. In n=36 for all time points of evaluation. At any point in time, between 30 and 50 mobilizations will be observed, so a total number of n= 90–150 observations are planned. The mobilizing professionals can be observed multiple times. All participants included in the study may withdraw their consent to participate in the study at any time.Interviews and observations will be performed at three different points in time to assess the behavior and experience of mobilizing professionals. Interviews will be carried out with at least four persons of every professional group  until data saturation occurs. The approximate sample size for interviews is The study covers the period of early mobilization by the robotic system of patients who meet the inclusion and exclusion criteria. These are mobilized with the robotic early mobilization device approximately twice a day for 20 min, or at least 10 times within 7 days. The data collection will take place for 5 to 6 months, beginning in September 2021. Three study series will be performed during the study period: (1) feasibility line of robot-assisted VEM in the ICU, (2) care line: behavior and experience of the mobilizing professionals , and (3) prom line: effects on patient outcomes.All patients will receive a physical examination at different time points to assess physical functionality and muscle strength, as well as a sonographic examination of leg muscles, diaphragm, and lungs. These examinations and the collection of clinical scores will be performed on day −1 (preoperatively); on postoperative days 1, 2, and 3, then once a week if the patient remains in the ICU; on day 28; on the day of discharge from the ICU; and on a follow-up examination within the context of routine examinations approximately 3 months after discharge from the ICU . The folThe evaluations of the behavior and experience of the mobilizing professionals and the feasibility line will be collected accompanying the robotic-assisted mobilizations of the patients. The survey ends with the last robotic-assisted mobilization. Observations of the professionals will only be performed with patients who have given consent to participate in the study. The informed consent of mobilizing professionals and evaluation will be conducted by nursing scientists and study physiotherapists.The feasibility of the study will be rated when the intervention group has at a minimum equivalent effects on the patient outcome as the historical comparison group, a minimum rate of robot-assisted mobilizations  can be performed, no serious adverse events occur, and feasibility is judged to be acceptable by the users.A unique three-digit ID will be assigned to each patient, under which all data will be recorded pseudonymously. All invasive procedures performed on patients will be carried out as routine procedures independently of the study in the ICU according to medical indication. The following study-related procedures will be performed on the patients beyond the informed consent and documentation of patient-related data. All required information collected in routine clinical practice is to be obtained from the patient documentation system (electronic patient record) Table .Table 2PWithin the episodic interviews , the strFor rating feasibility, minimum and maximum rates are used and shown in braces behind the parameters. Data on recruitment  and excluded patients), as well as retention rates (the number of patients with discontinued interventions/adverse events (max. 10%)/severe adverse events) and type of adverse events  are documented each week. In addition, the following intervention-related data will be collected: duration and set-up time of the intervention (max. 25 min in mean), number of mobilizing persons (<2 persons), degree of verticalization, minutes in the highest degree of verticalization, steps per minute, and minutes of intervention in total (20 min in mean), as well as maximum hip angle. The mobilizing professionals will rate physical stress (max. 3 in mean) and feasibility (min. 4 in mean) on every early mobilization that is performed on a 7-point Likert scale.Patients included in the study, according to the inclusion and exclusion criteria, will be mobilized using the robotic system by the nursing ward team. The aim is to perform a standardized mobilization with verticalization within the first 72 h after admission to the intensive care unit Fig. . If possRobot-assisted early mobilization is performed only if it is deemed safe according to the criteria and recommendations of the Consensus Conference . This CoThe mobilizing professionals are trained for 90 min in robotic-assisted mobilization with healthy respondents. From every participating ward, 9 nurses are participating in the training. Product specialists accompany the professionals in mobilizing study patients until they feel safe to operate the device by themselves. The research team accompanies the mobilizing professionals through the whole study in every mobilization. Users can be certified as super users who are qualified to train other nurses or physiotherapists within the use of the device, so that there is a snowball system in knowledge.The comparison group is a historical collective, which also meets the inclusion and exclusion criteria of the study. These patients were early mobilized following the ward routine of the intensive care unit’s conventional early mobilization according to the instructions of the treatment team, consisting of physicians, nurses, and physiotherapists. Conventional early mobilization cannot be precisely defined based on a retrospective study . The infOnly patients who are capable of giving consent and can be informed preoperatively will be included by the study physicians. Informed written consent will be obtained from all patients who meet the inclusion criteria. If the patients withdraw their consent at a later point in time, they will be asked whether the data collected up to this point in time may still be used. Otherwise, all data collected up to that point will be destroyed. There is no intention to include persons from the group of persons in need of special protection.To assess physical function and muscle strength, the following non-invasive examinations will be performed, and/or scores will be collected as required by the study:FSS-ICU : the FSSAt the follow-up examination approximately 3 months after discharge from the ICU, the health-related quality of life will also be assessed using the SF-36 questionnaire .By means of ultrasound, the following parameters are evaluated in the course of the study:The diaphragm is characterized by determining the diaphragm thickness, the thickening fraction, and the motility. The musculus quadriceps femoris is characterized by determining its thickness and by using the cross-sectional area. The methodology of the ultrasound examination is described in detail in the literature –34.The treatment team does not differ for the individual patients; it usually consists of nurses from the corresponding intensive care units, assigned physiotherapists, and the corresponding ward physicians. For the duration of the study, an additional study team will be established, consisting of study physicians, study nurses, and technical support from the manufacturer.Robotic-assisted early mobilization should be performed within the first 72 h postoperatively, if possible, and should be performed at least twice a day for 20 min until the seventh postoperative day or at least 10 cycles of treatment during the intensive care unit stay. The frequency of treatment, treatment duration, and intensity are recorded. Treatment-associated events will be recorded. In case of hemodynamic, respiratory, or other instability during treatment, the therapy session can be discontinued at any time. The decision to discontinue mobilization rests solely with the treatment team. The study team can advise and act in accordance with the Declaration of Helsinki.Within biweekly meetings of the operative working research team, the results and the ongoing of the study are evaluated.In the context of organizational feasibility, descriptive data is reported and visualized for robotic-assisted VEM. When minimum/maximum rates are reached, further analysis will be performed. Otherwise, the intervention is not seen as feasible. Subsequently, regression analyses are used to contextualize and quantify the data. Depending on the variables and the type of distribution, they are quantified after calculating degrees of freedom, and correlations are tested using applicable analysis methods.Interview data on stress experience and physical behavior will be collected within a robot-assisted mobilization situation and evaluated and visualized by means of qualitative content analysis . Data ofWithin the study population, conventional early mobilization of critically ill intensive care patients  will be compared with robot-assisted early mobilization. The data will be evaluated by graphical representations of the individual parameters in the course by means of box plots and scatter diagrams. Associations between parameters are quantified using appropriate  correlation coefficients. For comparison between conventional and robot-assisted VEM, commonly used robust statistical methods are applied.After 50% of the participants have been included in the study, the first interim analysis will be performed and is the basis for further decisions.For the entire project, an overarching data protection statement Art. 6 DSVGO  of the data protection officer of the LMU Hospital is available (Procedure Number 1582a of 13 July 2021).The data will be collected by means of digital questionnaires. The patients will receive a three-digit pseudonymized ID after giving their consent. Target criteria collected in routine clinical practice will be recorded with the routine case number and transferred to the research database created specifically for the project. Data monitoring is done inhouse by a biostatistical and bioinformatical institute. After completion of the documentation, the case number will be replaced by the above-mentioned ID. All personal data will be recorded under this ID. The data from the survey forms are promptly stored electronically in a secure folder. These are secured by the network of the participating institutions, and access to the data is restricted.Only the research team has access to the research database. Access to personal data (effects on patient outcomes) is restricted to the study physicians, who are bound by medical confidentiality. After the individual patient has completed the study, the personal reference is removed, and the encryption code is only kept in a written document in a lockable cabinet in the anesthesiological ICU, to which only the clinical study director has access. Decoding is only performed for the safety of the patients  or in case of a change of the scientific question (= scientific reasons). The regulations of medical confidentiality and data protection are observed in this study. Patients will be informed in detail about data protection during patient education. Access to study-related data is only possible via the respective study directors. All data will be destroyed according to the usual retention periods .Only the study team of the LMU Hospital and the Catholic University of Eichstätt-Ingolstadt (experience and behavior of the users) has access to the collected data.The names of the participants and all other confidential information are subject to confidentiality and the regulations of the DSVGO and the Federal/State Data Protection Act (BDSG/BayDSG). Data of the study participants will not be passed on. Third parties will not be given access to the original documents. The data collected during the study will be kept until the data analysis is completed and then destroyed. Pseudonymized data may be shared with scientific project partners as part of the discourse on the study.The study is designed as a clinical intervention study with comparison to a historical patient population. Patient participation is voluntary. The value of early mobilization in critically ill patients has been proven, as has the safety of early mobilization. Harmful events occurred very rarely in comparable studies, and serious adverse events seen in association with the study did not occur , 13. TheOtherwise, the study team has no influence on the treatment of the patients.The primary benefit in terms of effects on patient outcomes is to determine if robot-assisted mobilization differs from conventional early mobilization in its ability to reduce ventilation time, muscle atrophy, and ICU-acquired weakness. Individual patients could benefit from intensive robot-assisted early mobilization in terms of shorter ventilation, less muscle atrophy, and better physical functionality. A lasting negative impact on the patient group is not expected if treatment is performed with a safe, non-invasive medical device and intensive physiotherapeutic exercise. Serious adverse events associated with the medical device are not known. Possible adverse events such as short-lasting changes in blood pressure and heart rate, the accidental removal of drains, or the development of skin lesions due to the mobilization cuffs could occur.From the data obtained, improved therapy concepts can be developed, and the use of robot-assisted mobilization can be established as part of a new standard of care. This study makes a significant contribution to the future improvement of therapy for critically ill patients. If the measures of robot-assisted VEM prove superior to those of historical, conventional VEM, the new treatment technique could be quickly implemented into the clinical routine in ICUs based on this study.The data collected during the study is not available to the treating physicians during the patient recruitment phase. This way, no negative influence on the therapy of the individual patient can arise. Even if conclusions regarding the treatment of future patients or patients from other ICUs cannot be drawn from the data obtained in an unlimited and uncritical manner, the study presented here provides a valuable gain in knowledge with the aim of comparatively examining different forms of early mobilization of the effect on a specific patient population. By simultaneously surveying the experience and behavior of the mobilizing professionals, it is also possible to record their workload when using the new therapy. Given the high workload in ICUs, a feasibility study is essential, which is why the study focuses on users, patients, and structures. All participants in this study  gave informed and written consent to the interventions before including in the study.In summary, this study makes an important and necessary contribution to improving the therapy of critically ill patients. There are no study-related burdens for the individual patients, and participation in the study is without risks for the patients.The study directors, as well as all participating scientists, commit themselves to conduct the study described herein in accordance with the study protocol. Changes to the study protocol are only possible after consultation with the ethics committee; if necessary, a new evaluation will be obtained. In case of severe adverse events or violation of the participants, the study will immediately be stopped by the study management.This evaluation will provide new insights for implementing a robotic device into ICUs, concerning patient outcomes and the feasibility as well as potential effects for mobilizing professionals. If the effects on patients show a better rehabilitation outcome concerning muscle loss and routine parameters, this new technology might reduce the duration in intensive care and in the hospital. Since the device can also be used to mobilize sedated or immobile patients at an early stage, there is a chance that mobilization frequency can be increased. Small studies have already shown the benefit of early mobilization in the care of strokes , 37. TheThe aim of the study is to evaluate whether a new standard of care can be implemented in the intensive care setting with a robotic system and whether the setting with its current structures could implement this standard of care permanently. For this, it is essential that at least the same outcomes of mobilization are generated for patients, that the mobilizing professionals experience a positive effect, and that time and staff retention can be implemented in the intensive care unit.The study is performed with a small number of patients. Results in patients who experience other serious illnesses may differ. The study is single-centric, so the data depend on staff and patients, and external validity is reduced. The survey of effects on patient outcomes will mainly examine parameters recommended in the literature. Possible other parameters might show other effects. The results of the interviews of the mobilizing professionals might vary due to the qualitative approach. The observations are performed by several researchers, which might influence the continuity of data.The results of the study, as well as results from the respective surveys, will be made available to the public subsequently. This will be enacted in the form of publications and in contributions on scientific conferences.


Trypanosoma brucei gambiense. The dose range was determined in phase 1 studies and a significant food effect was observed, which was tested with field-adapted meals. The pharmacokinetic profile required definition of a higher loading dosage for the first 4 days and administration of the daily dose together with a typical local meal to optimize product absorption and rapidly achieve drug steady state. This allowed for a combined phase II/III pivotal study directly after phase I trials. Partnerships with highly engaged actors from endemic country control programs and international research institutions started early through the HAT platform, building on an agreed target product profile (TPP), establishing a regulatory plan early and transparently including endemic countries in the research and data flow. A key element that enabled a quick start to access activities was preparing for World Health Organization guidelines early and starting the process prior to registration. Distribution plans were identified and supply was established from the start, by taking advantage of the existing supply agreement between the producers of all HAT drugs (Sanofi and Bayer) and the WHO. Pharmacovigilance and phase 4 studies were nested into wider implementation activities. Targeted sequential introduction into national programs was prioritized, based on medical need and epidemiologically updated information.In this article, the authors show the strategy used to streamline the introduction of fexinidazole, the first all oral treatment of human African trypanosomiasis (HAT) caused by  Trypanosoma brucei: T. b. rhodesiense (r-HAT) and T. b. gambiense (g-HAT); the disease is transmitted through the bite of a tsetse fly.Human African trypanosomiasis (HAT), or sleeping sickness, is a disease caused by two subspecies of the unicellular flagellated parasite Without treatment, patients will invariably progress from the first to the second stage of the disease, which is when parasites have invaded the central nervous system. Therefore, treatment at that stage needs to cross the blood–brain barrier effectively.Melarsoprol, an arsenic-based treatment, was introduced in 1949 for stage 2 HAT. This injectable drug, described as “fire in the veins” by patients, was highly effective and lifesaving, but is associated with encephalopathy in 10% of patients, and is directly lethal in 5% of patients.initiative (DNDi) initiated a phase 3 study comparing a shorter combined regimen of eflornithine (only two infusions per day for 7 days) plus nifurtimox, an oral treatment already registered for Chagas disease (caused by American trypanosomes), given three times a day for 10 days. This shorter treatment is known as nifurtimox-eflornithine combination therapy (NECT). NECT was a simpler regimen that showed similar efficacy to eflornithine alone as well as good safety in stage 2 HAT. It therefore in 2009 became the reference for the treatment of stage 2 HAT. Nifurtimox-eflornithine combination therapy was subsequently added to the World Health Organization (WHO) Essential Medicines List and, following extensive training by WHO and national sleeping sickness control programs (NSSCPs), was rapidly deployed in countries.In the early 2000s, Doctors Without Borders/Médecins Sans Frontières (MSF), Epicenter, and later the Drugs for Neglected Diseases Meanwhile, little progress was made on treating patients at the earlier stage of the disease, where the first-line treatment was mostly intramuscular pentamidine.  for HAT drugs in 2010. One of the major requirements was a stage-independent and oral-only treatment. An oral treatment would be better adapted for use in the contexts where most HAT patients live: it would enable HAT treatment to be delivered on an outpatient basis, and require hospitalization only for those needing specialized management or monitoring.i selected fexinidazole for further preclinical development.After identification of the nitroimidazole family as potentially active against HAT, an extensive compound mining effort, searching among more than 700 nitroheterocycles, resulted in the rediscovery of fexinidazole. This broad spectrum antiparasitic was initially developed in the 1970s but later abandoned. Its in vivo activity against HAT had been identified in 1983,i and Sanofi, signed in 2009. Third, a regulatory strategy was devised to ensure quick registration in endemic countries and hence rapid access to patients. Sanofi and DNDi selected the combined approach of requesting 1) a scientific opinion from the European Medicines Agency (EMA) Committee for Medicinal Products for Human Use through the Article 58 procedure  for products intended to prevent or treat diseases of major public health interest and for use exclusively outside the European Union and 2) inclusion of the product into the Essential Medicines List and WHO prequalification on the basis of assessments and inspections conducted by EMA. The positive opinion from the Committee for Medicinal Products for Human Use was the basis for facilitating registration in DRC and Uganda and subsequent approval by the Ministries of Health of the remaining g-HAT-endemic countries. Finally, the study progress and the access strategy were discussed regularly at WHO HAT stakeholder meetings and by the specific working group on integration of new tools.Several factors enabled the acceleration of the fexinidazole timeline from development to introduction. First, work in partnership with all stakeholders to achieve and maintain alignment on fexinidazole’s medical value started early, at the time of the TPP, ensuring that a new treatment would respond to the needs of the patients and have a feasible rollout. Second, manufacturing and distribution were supported by an agreement between DNDi enabled the HAT platform to organize training sessions, engage local authorities from Ministries of Health to facilitate access to the new treatment, and promote pharmacovigilance. Annual meetings were frequently combined with scientific events and were useful for sharing new research developments in the HAT field among the partners. As a result, the HAT platform had a fundamental role in keeping all relevant partners informed about the progress of the clinical research. The HAT platform board had a central role both in defining the TPP that led to the selection and development of fexinidazole and in keeping the governments of endemic countries aware of the progress and results of the clinical trials.The main objective of the HAT platform was to facilitate the research environment by increasing technical capacity and to prepare for future access to the treatments developed through local research.i and Sanofi reached an agreement to jointly develop fexinidazole, with DNDi in charge of the clinical development and Sanofi responsible for product registration, industrial development, and further regular supply, if successful. The DRC NSSCP, and other NSSCPs from the HAT Platform, were key partners throughout the process, from study development and implementation to fexinidazole training and finally rollout and surveillance. In fact, the NSSCPs of DRC, Central African Republic, and Guinea led the choice of study hospitals, based on the reported HAT endemicity, and managed the research teams for each hospital via a coordinating investigator who ensured stability and harmonized the trial activities by providing regular supervision. All partners were regularly updated and consulted via regular meetings organized by WHO and HAT stakeholders, as well as meetings of a specific working group focused on development of new tools, which discussed diagnostics and treatment needs and advances.In 2009, before starting phase 1 studies, DNDi, with the support of WHO, conducted a joint ethics prereviewi and Sanofi project teams; this received a fee exemption and accelerated review timelines. A positive scientific opinion was granted by the EMA Committee for Medicinal Products for Human Use on November 15, 2018, after two rounds of questions and answers.The EU-M4all procedure of the EMA has the advantage of being in collaboration with WHO and regulators from targeted HAT-endemic African countries, which in the case of fexinidazole were from DRC and Uganda. The aim of the procedure is to streamline review of the dossier once it is presented to the countries. Two scientific advice meetings were arranged to review the phase 2/3 pivotal trial protocol and to propose two complementary cohort studies to widen the populations studied. Before the study, DNDThe marketing authorization approval in DRC was granted in 2018, 6 weeks after the positive opinion from the EMA Committee for Medicinal Products for Human Use.i supported the NSSCPs to extend the training to all staff from 250 selected health structures that would deliver HAT treatment.The choice of target countries and priority actions was based on the prevalence of reported HAT cases and started with the DRC, which reported 82.5% of all HAT cases in the last decade (2011–2020). The process for country adoption began after fexinidazole received authorization for use in DRC. Subsequently, WHO engaged the Ministries of Health of all g-HAT-endemic countries to give a special authorization for use based on the DRC registration. By July 2021, this authorization had been obtained from seven additional countries. The initiation of the WHO guidelines development process early on, as soon as the study results had been published, 1 year before registration facilitated the accelerated pace toward introduction and for the guidelines to be released in August 2019. The participating countries reviewed and adapted these guidelines and adopted national ones. The WHO HAT team then organized two sessions to train trainers. Through the HAT Platform, DNDinitiative’s support to pharmacovigilance reporting is actively concentrated in the most endemic or difficult-to-reach countries and areas. Regular supervisory visits have been organized in the DRC and will be proposed for Guinea, Central African Republic, Angola, and South Sudan.The EMA requested Sanofi to conduct a post approval safety study, and data are now being gathered by the NSSCPs, with technical support from WHO to provide additional safety information to that which was obtained from the relatively low number of participants included in the clinical trials. This new data on real-life safety and effectiveness of fexinidazole will be a retrospective analysis over 3 years, from March 2020, when the product was first supplied to DRC, until February 2023, and including all HAT-endemic countries that have detected cases, once fexinidazole is delivered to each of them. In addition, a simplified pharmacovigilance reporting system and tools were prepared, provided to all NSSCPs, and included in the training of prescribers. Drugs for Neglected Diseases Human African trypanosomiasis treatment distribution was easy to organize. This was the result of agreements between Sanofi and WHO, in place since 2001, to provide pentamidine, eflornithine, and melarsoprol to the endemic countries in a centralized way and at no cost. A similar agreement exists with Bayer for nifurtimox and suramin. Each country provides its epidemiological data and discusses with the WHO team its needs for the coming year. The World Health Organization informs Sanofi and Bayer of the collective needs, then both companies forecast manufacturing needs and provide the drugs to the MSF Logistique supply center, which has agreed to store the drugs and organize the international aspects of drug distribution. With the authorization of WHO, the MSF Logistique supply center can also provide fexinidazole to international or national nongovernmental organizations who have justified the need. Each country then requests a specific supply for all drugs to the WHO HAT team, who then requests the MSF Logistique supply center to send the requested drugs to the relevant NSSCP. The much smaller volume of materials needed for fexinidazole treatment compared with NECT (which requires infusion sets and bags) simplifies the supply process and reduces transport costs. The fact that fexinidazole could fit into this previously existing system removed any potential delays related to setting up new systems for procurement and supply.The introduction of NECT was rapid for two main reasons. First, it replaced the highly toxic melarsoprol as a first choice for treatment, and second, because of the need for intravenous infusions of eflornithine, the target health structures for NECT were general reference hospitals. Nifurtimox-eflornithine combination therapy’s better safety profile, quick addition to WHO’s Essential Medicines List, training for prescribers and treating nurses, and use of existing donation and distribution programs were the operational elements for success. The main strength of fexinidazole compared with NECT has been the transformation of g-HAT into an “ordinary” disease, with no requirement for lumbar puncture or to travel for specialized hospital care, except for patients with advanced stage disease (who require NECT) or children under 6 years old or weighing less than 20 kg (who remain ineligible for fexinidazole treatment). Only by simplifying case management procedures we can transfer the responsibility of care from a vertical program into the general health care structures, allowing the disease to be integrated into the standard primary health care activity package. The next challenge, then, was of introducing HAT treatment into regular peripheral health structures that had no previous experience in diagnosing or treating HAT. A large training program directed at the staff of 250 selected peripheral centers in the targeted countries had to be designed to familiarize them with the clinical symptoms, diagnostic algorithm, and treatment administration and monitoring requirements.i. The first step, taken by WHO, was to train the trainers from the endemic countries in the clinical characteristics of the disease, its treatment, and pharmacovigilance. These trainers were subsequently supported by DNDi resources to organize cascade training of chosen practitioners at the national level, starting in the most endemic countries, with particular focus on the DRC, as it has the largest and most complex needs because of the high relative prevalence of the disease in comparison with the rest of the endemic countries. A related challenge was the need to accompany direct fexinidazole training with improvement of screening and diagnostic capacity for the selected peripheral health structures. A parallel training program directed toward laboratory personnel at these health structures was designed and implemented in DRC. Finally, the poor status of the health system structures themselves meant that up to 30 peripheral health structures in DRC had to either be built or rehabilitated and then equipped to diagnose HAT.To reach over 500 remotely based personnel from the chosen health structures, cascade training was designed in close coordination with the WHO HAT team, the NSSCPs, and DNDAnother challenge specific to HAT is that its reduced prevalence and nonspecific symptoms often leads to delays in health-seeking behaviors, which in turn result in a long and slow pathway to correct diagnosis of infected individuals. Additionally, there is a low level of awareness of the risks of undetected disease. Therefore, it became clear that social science interventions were needed. Ethnographic studies qualitatively assess communities’ perception of the disease, as well as related health-seeking behaviors.Yet another challenge to the introduction of fexinidazole was that it required a complex preliminary setup, including a three-step process that started by generating treatment guidelines and training the trainers. Then followed the adoption of the new oral treatment, including translating WHO guidelines into national guidance. Launching the supply required both formal authorization of the use of fexinidazole by those governments that had not approved a regular market authorization and training of staff involved in fexinidazole administration. Finally, the national authorities requested the drugs, which were donated by Sanofi, authorized by WHO, and supplied through MSF Logistique. Fexinidazole was introduced into different countries sequentially, starting in DRC. Since the supply is centralized by WHO, which forecasts production according to national reports, and there is a small number of patients to be treated, there have been no relevant product supply issues. In DRC, because of the large geographical distribution of HAT and the small number of cases in each focus, the NSSCP decided to store fexinidazole at the provincial coordination level, enabling the drug to be sent to the selected treatment center when new cases were identified. Scale-up activities were severely disrupted in places by the COVID-19 pandemic, which arrived at the time of the cascade training and delayed several other activities. In addition, COVID-19 had a critical impact on screening activities, which resulted in fewer patients screened and only 663 diagnosed in 2020, which was lower than expected.By June 2021, 200 patients had been treated with fexinidazole in four countries. Most of them were treated in DRC but also, in Chad, Guinea, and Central African Republic. Since its introduction in each country/province, the treatment coverage with fexinidazole has been slightly above 50% of all cases. This coverage is directly in line with fexinidazole being indicated as the first-line treatment, except for 1) patients with neurological symptoms and over 100 white blood cells per µL of cerebrospinal fluid who require the use of NECT or 2) children less than 6 years old or weighing less than 20 kg who require pentamidine or NECT according to the severity of the disease. New countries with lower endemicity are being trained and will receive the new treatment once the requirements  are fulfilled. The potential impact of fexinidazole depends on local case-finding capacities. Information, education, and communication activities in endemic areas remain important to create awareness in communities and encourage potentially affected patients to get diagnosed and treated; these activities also clarify and dispel existing myths about the disease and the remaining fear from the toxic arsenic-containing treatment. Training of prescribers needs to be continually available as the existing health staff may turnover, and new staff arriving to the identified treatment centers may not be aware of HAT and the appropriate treatments.The disease is on its way to elimination. The number of detected cases has reduced from 38,000 in 1998 to less than 1,000 annually since 2018. The downside of this great success is that young health professionals are not interested in specializing in HAT and the existing expertise is fading away as workers involved in HAT diagnosis and treatment retire. Governments of HAT-endemic countries will reduce their attention on combating this disease as they have many urgent conflicting priorities in healthcare and little dedicated budget. International attention and financial support may fade if elimination has not occurred by the target date for absence of cases in 2030. This decrease in priority, attention, and resources as cases continue to decline could put the goal of elimination at risk.Most countries in sub-Saharan Africa with endemic diseases have weak health systems, difficult physical access to specialized treatment structures, and high costs, even if the diagnosis and treatment of a condition is given free at the point of delivery. It was essential to consider this context from the start of the development and access activities and examine the local blocking points to provide tailored support that would increase accessibility.Training cannot be delivered just once but requires careful follow-up to detect and correct weaknesses and to support newly deployed health personnel. Training cannot only be technical, centered on specific trial good clinical practice or medical needs; training must include all aspects related to project management, including financial management, supply logistics, timely requests to renew consumables, human resources management, and issues related to strict respect of standard precautions related to hygiene and waste management.Energy and telecommunications needs must be carefully assessed, and bottlenecks solved. It is highly advisable to have a backup solution on hand for most elements at risk, for example, by combining different sources of electricity generation or alternative methods of telecommunication.The research team must communicate in two directions that are not always considered relevant: with the communities and with local authorities. The communities must understand the reasons for the research and the impact the results may have on their health. Once development is completed, the research team needs to share results with the community to generate demand for the new tool. The research team also needs to communicate with local authorities, and this communication needs to go beyond the usual ethical and regulatory review processes, as authorities will need to understand that moving staff around during a clinical trial or after a training effort will directly affect the quality of services provided. Furthermore, the research team must have a clear understanding of the needs for, and perceptions and expectations of, the new development. Social science research on people’s prevailing understanding of the disease, existing beliefs, its treatment, and health-seeking behavior will be helpful, especially during the access phase of the project.Despite the challenges, there are clear advantages to compressing development and introduction timelines to rapidly bring an improved solution to a problem. There are two main aspects to trying to reduce development and access timelines. All administrative steps should be carefully planned so that a blockage in one activity is identified and resolved with the lowest possible impact on the critical path. Time and resources are needed to streamline these processes but are not always available or considered a priority. Scientific advice obtained from stringent regulatory agencies about the minimum requirements for the study design, as well as necessary additional studies or pharmaceutical product development steps, will help to provide a clean dossier and facilitate the approval timeline. A simple but comprehensive dossier is always key to success.The second element needed to rapidly complete a study that will give relevant results is a thorough epidemiological assessment before enrolling patients. During the development of fexinidazole, the scarcity of cases and steady reduction in numbers of cases made it necessary to locate the most endemic areas and upgrade existing health structures so that patients would be found and included in the trial. During the trials, slow recruitment required the opening of new trial sites to adhere to the planned timelines as closely as possible. This problem is specific to an increasingly rare disease such as HAT, but other treatments may also be affected if the preliminary assessment does not carefully consider the inclusion and exclusion criteria, as not every case of a disease will be eligible to participate in a clinical trial. Specific attention needs to be given to women of reproductive age and the feasibility of protecting them from unwanted pregnancies during the trial.It is not straightforward to generalize the experience of developing a new drug for HAT to other interventions, as many difficulties and risks will be specific, both epidemiologically and geographically. However, some elements can be generalized. Our experience has shown that it takes longer to gather funding, prepare proposals, and submit them to ethics and regulatory authorities than the actual execution of a development program. In addition, careful planning and engagement of key stakeholders from the start is critical. Anything that was not considered during the planning period will certainly delay your product development. Furthermore, unexpected challenges may delay even the best-laid plan, such as the disruption of introduction activities by the COVID-19 pandemic. Nonetheless, any effort to compress timelines is worthwhile as it will accelerate access to new or improved public health interventions to the populations that need them the most.


Recently, IT technologies related to the Fourth Industrial Revolution (4IR), such as artificial intelligence (AI), Internet of things (IoT), cloud computing, and edge computing have been studied. Although there are many used clothing occurrences with 61 trillion worn of clothing consumption per year in Korea, it is not properly collected due to the efficiency of the used clothing collection system, and the collected used clothing is not properly recycled due to insufficient recycling system, lack of skilled labor force, and health problems of workers. To solve this problem, this study proposes a deep learning clothing classification system (DLCCS) using cloud and edge computing. The system proposed is to classify clothing image data input from camera terminals installed in various clothing classification sites in various regions into two classes, as well as nine classes, by deep learning using convolution neural network (CNN). And the classification results are stored in the cloud through edge computing. The edge computing enables the analysis of the data of the Internet of Things (IoT) device on the edge of the network before transmitting it to the cloud. The performance evaluation parameters that are considered for the proposed research study are transmission velocity and latency. Proposed system can efficiently improve the process and automation in the classification and processing of recycled clothing in various places. It is also expected that the waste of clothing resources and health problems of clothing classification workers will be improved. Recently, IT technologies related to the Fourth Industrial Revolution (4IR), such as artificial intelligence (AI), Internet of things (IoT), cloud computing, and edge computing have been studied. AI, led by machine learning and in-depth learning, has emerged as a key technology of 4IR. Various research and development related to AI are being carried out in various industrial fields. It is predicted that artificial intelligence will not be used only in the medical field, but will be used in personal  health care along with the development of ICT technology.Clothing image analysis technology has been applied to recommend or find specific clothing –5. The aThe textile industry consumes a lot of human resources in all processes such as raw material collection, dyeing, processing, and sewing, and the waste of resources and energy and the increase of environmental pollution are caused by the short-term waste of clothing produced through this process. For example, 1,500 liters of water is used to make a pair of jeans, and 10 to 15% of the chemicals that come out after weaving and removing dyes become wastewater and pollute the environment. If you buy clothes at a second hand shop, which sells used goods instead of buying new clothes, you can reduce environmental pollution as much. Therefore, there are many efforts in domestic and various countries to recycle and rewear waste clothing if possible. Although there are many used clothing occurrences with 61 trillion worn of clothing consumption per year in Korea [Edge computing enables real-time decision making by minimizing delay time and bandwidth requirements because it can process more data in the network ‘edge' near the data source and reduce the data transmitted to the cloud data center. Cloud computing has its own advantages and importance for big data analysis and computation but have some disadvantages also in regards to real time and time sensitive data computation.To improve the problem of data transmission networks, it is important that the combination of edge computing and cloud computing can provide you reduced disadvantages of cloud computing. IoT devices with edge computing and cloud computing will be able to run faster and process data efficiently without losing storage capacity and processing power. , 8.In this study, to make an offer solution about the problem, I suggest a deep learning clothes classification system (DLCCS) using cloud and edge computing for efficient classification of recycled clothing. For the proposed study, first, the AI model AlexNet was used, IoT devices, camera and raspberry pie 3, was used, and clothing was classified into nine classes . The accuracy of the classification was measured and the usability of the proposed system was confirmed and announced . The sysAccording to , 100 bilThe clothes in the recycled clothing box are first collected by the old clothes collection box management company or individuals periodically . These old clothes come in 40 tons per day to the collection company, and there are nearly 100 collection companies in Korea, that is, more than 4,000 tons of clothes are discarded in Korea per day. Of the clothes collected, only 5% of the clothes that are still commercial are distributed in ‘Vintage Shops' and ‘Relief Shops' in Korea and sold again, and all remaining 95% are exported overseas. Most of them are imported from developing countries, which is the fifth largest exporter of used clothing in the world, and they incinerate all clothes that are not really commercial.As shown in Waste clothing collected by separation, and discharge is classified as reusable and impossible. Reusables are sold in flea markets, bazaars, etc., in Korea and exported to foreign countries including underdeveloped countries.Edge computing is a distributed open architecture with distributed processing capabilities that enables mobile computing and IoT technology. The role of edge computing to date has been mainly used to collect, store, filter, and transmit data to cloud systems. Edge computing allows data from Internet of Things devices  to be analyzed at the edge of the network before being sent to the cloud –13. EdgeTo simply define the most basic concept of cloud computers, one of the IT technologies of 4IR, when you do any work or work on a personal computer, simply input/output it to bring a program to a server called the cloud based on the Internet, and the analysis, processing, and management of the data are also format in the cloud space –16.Cloud computing includes all ranges of IaaS (Infrastructure as a Service) that serves server resources to run servers, PaaS (Platform as a Service), which provides development platform middleware, and SaaS (Software as a Service) that serves applications.IoT and Edge computing and Cloud are shown in AI, led by machine learning and in-depth learning, along with new technologies leading the 4IR, is applied through various research and development. Deep learning is a subclass of machine learning that uses artificial neural networks for tasks such as visual recognition and is used to train the computer to classify images based on clothing content. Deep learning neural networks include one or more hidden layers.A CNN is used as a popular deep learning technique to analyze visual images to apply deep learning framework and is a neural network made by using the concept of a receptive field of a human visual neuron. CNN has a convolutional layer, pooling layer, and a rectified linear units as core elements. The convolutional neural layer is characterized by a weight kernel that sees the input and output in the form of a signal and shows the weight in the form of a small filter. The pulling of the nerve layer has the role of reducing the size of data, as it summarizes a few output values. And the effect of canceling the noise or the distortion of input data in this process may be obtained. The rectifying linear unit is a nonlinear neuron with the activity of a lamp function, and has the effect of simultaneously solving the calculation burden of the sigmoid function used in the neural network and the slope disappearance phenomenon in the reverse wave algorithm. In 2012, AlexNet is a CNN architecture and consists of eight learning, five convolutional, and three fully connected layers , 18. SinResearch on CNN-based clothing recognition often focuses on fashion classification or detecting clothing by processing surveillance camera footage –26. HoweFirst, demonstrate clothing classification using large scale dataset, where the proposed model performs. Second, the results show that the model correctly classifies most of the test images with a success rate that is higher than 70%. Finally, we evaluate clothing classification using footage from surveillance cameras. The system performs well on this dataset, labelling about 65% of the test images correctly. The process of classifying the clothes image dataset using CNN is called a Deep Learning Clothes Classification System (DLCCS) and is shown in Figures The proposed system can be installed in various places where recycled clothing is collected, and the proposed system classifies the clothing image data set input from the terminal into several classes using CNN. Classes can be classified as large class and small classes. Large classes are top and bottom, adults and children, and small classes are knit, cardigan, coat, pants, skirt, etc. The classification results are transmitted and stored in the cloud through edge computing. To solve this problem, many DLCCS are composed of IoT devices and CNN and are connected to cloud using Edge computing to build a recycled clothing classification network. Edge computing is a technology that processes data in real time near the site where the data is generated, and it is the opposite concept of cloud computing that processes data through servers on the Internet. Suggestion of DLCCS using Edge computing and Cloud computing is shown in In LTotal-C) is L1+L2+L3. Delay time (L1) is generated when the clothing image information is transmitted from the node to the cloud, and the deep learning classification and judgment time (L2) are added to the cloud, and the delay time (L3) is generated to return the result to the node again. In LTotal-EC) is reduced. In particular, the delay in the gateway existing in the network channel and the congestion delay between each node will be added as the number of nodes increases. In the case of a wired channel, an optical network was simulated, and in the case of a wireless channel, a 5G network was simulated. The channel is wired using MATLAB, the simulation conditions are shown in The simulation is shown in Figures Computing the network channel model for simulation is as follows:Tolal-C by using equation , but the increase in congestion due to the increase of nodes showed that the wireless network environment was more frequent. The result of equation  was smaller with the configuration of the proposed model using the 5G network. On average, LTotal-EC (5G) was reduced about 8 times compared to LTotal-EC (O).In Recently, IT technologies related to the Fourth Industrial Revolution (IR) such as artificial intelligence (AI), Internet of Things (IoT), cloud computing, and edge computing have been studied. As AI technology is gradually developing, the demand for machine learning work with smart devices is increasing rather than remote data centers, and edge computing will be an essential technology for AI-based Internet of Things. Edge computing is a technology that complements the shortcomings of cloud computing. Edge computing and cloud computing coexist and various services are being provided to complement each other.In Korea, recycled clothing is not properly collected due to the inefficiency of the used clothing collection system, and the recovered clothing is not properly recycled due to the lack of recycling system. In this paper, I proposed a deep learning clothing classification system using cloud and edge computing to classify recycled clothing. Instead of classifying and storing clothes using CNN in the cloud as shown in In the DLCCS configuration of Figures Because sorting recycled clothing requires a small amount of data and fast processing, edge computing is used, and a lot of data obtained in this process is accumulated in the cloud with high computing power. Therefore, the proposed DLCCS system can be integrated into the cloud for sorting and processing recycled garments in multiple locations to efficiently improve the process. In addition, in garment sorting workshops, the proposal system can send notifications of abnormalities or malfunctions to managers when intelligent sensors are installed in IoT devices. It is also expected to improve the wastage of clothing resources and the health problems of clothing sorting workers.


This paper constructs a decoupling model for four major economic regions of China, based on the Tapio decoupling index method and VAR model for carbon emissions to compare and measure the impact of decoupling between carbon emissions and economic growth in China during 1997 to 2019. The results show that the degree of decoupling between economic growth and carbon emissions varies among different economic regions, and the decoupling status is better in all regions at the beginning of the 21st century. In general, the decoupling status in the eastern and western regions is more ideal than that in the central and northeastern regions. The impulse response and variance decomposition results show that renewable energy consumption could always reduce the growth of carbon emission intensity, and its effects are most significant in the western region. The above findings help to reveal the link between economic growth, renewable energy consumption and carbon emissions in China in recent years, and how to ensure a stable economic growth in China while increasing the share of clean energy consumption in each region to achieve carbon neutrality. Economic growth cannot be achieved without significant energy consumption . However2 emissions [2 emissions in China.Carbon emissions are closely related to the type of energy consumption. The elasticity of carbon emissions to total oil consumption is positive and significant ,10. In amissions . Therefomissions . In contmissions . Howevermissions . This imBased on the Kuznets’ inverted U-curve hypothesis and the decoupling theory of carbon emissions, the decoupling between economic growth and greenhouse gas emissions gradually occurs when the economy develops to a certain level ,17. For The research contributions of this paper are as follows. Firstly, this paper provides new evidence to the existing literature on the decoupling of economic growth from carbon emissions in China’s four major economic regions, and the dynamic effects between renewable energy consumption and carbon emission intensity. Secondly, this study constructs a decoupling model based on the accurate description of carbon emission-related indicators in different regions, which could compare and measure the decoupling effects between carbon emissions and economic growth across the regions, while introducing carbon intensity as an indicator to further verify the impact of renewable energy consumption in the context of environmental protection. Moreover, the dynamic VAR model has the advantage of better reflecting the effects of renewable energy consumption and carbon intensity in different regions. Finally, this study analyzes the actual effects of renewable energy development on energy conservation and emission reduction in each region over 23 years from a spatial perspective based on the consumption structures of renewable and non-renewable energy of the four major economic regions in China. Its advantages are to measure the impacts of carbon emissions and carbon emission intensity according to the difference of energy consumption statuses in different economic regions, and to propose carbon emissions policy recommendations according to local conditions.The conceptual framework of this paper is shown in In the past five years, China has become the world’s top trading and industrial country. Especially since the outbreak of the COVID-19 in 2020, with the gradual recovery of China’s economy, carbon emissions and energy consumption have increased significantly. In general, according to Annual Report on China’s Energy Development (2022), coal combustion accounts for 56% of energy consumption, but major renewable energy consumptions, such as solar and wind, register at a relatively lower share. For instance, in provinces such as Sichuan and Yunnan, the energy consumptions are mainly based on hydropower, which is environmentally friendly, but the supply stability of energy is weak and vulnerable to unexpected incidents such as ecological or geographic changes. For example, power restriction policies in Sichuan and Northeast China in the summer of 2022 had a negative impact on economic growth. There is a relatively rich literature available to trace the dynamic relationship between energy consumption, economic growth and carbon emissions, which are reviewed as follows.Most scholars believe that decoupling is not yet happening in a significant way in many developing countries. The level of economic development has a key offset to the decoupling process, while energy intensity plays a significant role in promoting decoupling . In deveMoreover, economic growth has been a main driver of carbon emissions growth in recent decades, and the optimization of energy supply and consumption structures has had a significant dampening effect on carbon emissions . In termTherefore, increasing R&D investment in renewable energy has a positive impact on decoupling economic growth from environmental pressure. In China, this impact of increasing R&D in renewable energy is most pronounced. Other factors, such as urbanization, population density and the stability of renewable energy supply, could also influence the decoupling of economic growth from carbon emissions . For exaThe current overall level of carbon reduction technology in China is low, providing limited contribution to decoupling economic growth from industrial energy carbon emissions . China’sIn the view of some scholars, economic growth is the main factor for the increase of carbon emissions . There i2 levels [From the perspective of carbon emissions reduction, the uncoordinated regional economic development in turn affects the interregional differences in national environmental policies in China . At the 2 levels . The Kuz2 levels .First of all, energy markets affect energy consumption demand through energy prices, which in turn affects carbon markets and influences carbon emissions. There is a bidirectional causal relationship between renewable and non-renewable energy consumption and economic growth ,47,48. DIn addition, the volatility of the oil market would increase the risk of the natural gas market , and theIn terms of contents, unlike most previous articles, according to the division of China’s four economic regions by the National Bureau of Statistics, this paper makes use of the time series data of different regions to conduct research from a macro perspective, so as to better put forward policy suggestions by comparing the situation of different economic regions. This paper does not study the dynamic relationship between energy consumption, carbon emissions and economic growth, as most other literature does by considering China as a whole. Four economic regions are divided in detail in this paper. Based on the decoupling relationship between renewable energy consumption, carbon emissions and economic growth in each economic region, this paper studies regional green development and puts forward suggestions. This makes up for the lack of empirical research on renewable energy consumption in sub-regions. Moreover, there are many papers on the relationship between renewable energy consumption, carbon emissions and economic growth, but they ignore the relationship between renewable energy consumption and carbon emission intensity. This paper makes up for this gap. Additionally, different from previous studies, this paper constructs a dynamic VAR model to study the relationship between renewable energy consumption and carbon emission intensity.In this paper, based on National Bureau of Statistics, according to the new situation of China’s accelerated economic and social development, China is divided into a northeastern region, eastern region, central region and western region. Each region is composed of different provinces, which have certain differences in geographical location, energy structure and economic development. There are no independent judicial and legislative powers in different provinces. Moreover, the administrative power needs to be influenced by the superior, so the country should formulate different development strategies according to the local conditions of different regions. Time series data from 1997–2019 are selected for each economic region, mainly from the China Energy Statistical Yearbook (CESY) and the Carbon Emission Accounts & Datasets (CEADs). The China Energy Statistical Yearbook is a data book that comprehensively reflects the balance of China’s energy construction, production, consumption and supply and demand, and is mainly edited by the Department of Energy Statistics of the National Bureau of Statistics. The carbon emissions of the four major regions in China are calculated based on China’s emission apparent accounting method by referring to the research of Shan et al. . Data frDue to the great differences in economic development, energy policies and carbon emissions in different regions of China, this paper divides China into four economic regions, using the Tapio decoupling model to study the decoupling of economic growth and carbon emissions in different economic regions, and makes a comparative analysis in horizontal regions and longitudinal time. At the same time, many studies have proved that the development of renewable energy is beneficial to environmental protection, carbon reduction and sustainable environmental development. However, due to the constraints of environmental resources, geographical location and other factors in different economic regions, the consumption of renewable energy in different regions varies greatly. Therefore, this paper introduces the variable renewable energy consumption in four economic regions. Since decoupling means reducing environmental pressure during economic growth, in order to better measure this index, this paper introduces the variable ‘carbon emission intensity’, namely the carbon dioxide emission per unit of GDP. Then, this paper uses the impulse response function based on VAR model to study the dynamic effect between renewable energy consumption and carbon emission intensity by region.T is the decoupling coefficient, t + 1 and t and t + 1 and t, respectively.The term “decoupling” originates from the field of physics and is mainly used to describe the coupling state between variables. Decoupling analysis has become a hot topic of research in the field of resources and environment; “decoupling” means to reduce the consumption of material resources or environmental pressure while maintaining economic growth. This method has been widely used in different studies. For example, Zhang et al.  used theAccording to the different decoupling coefficients, decoupling is generally divided into three major decoupling types, and eight decoupling statuses shown in . The mosVAR is commonly used to describe the effects of stochastic perturbations on a system of variables. The modeling idea of VAR model is that each endogenous variable is constructed as a function of the lagged terms of all endogenous variables in the system. Then, it can be extended to a vector autoregressive model consisting of multivariate time series variables to estimate the dynamic relationships among all endogenous variables. This paper analyzes and predicts the degree of mutual shock impact between renewable energy consumption and carbon emission intensity in each of the four major economic regions by building a dynamic VAR model; the basic form is as follows:In order to eliminate the heteroskedasticity problem of the time series, the variables RE and EIC are logarithmically processed, which can reflect the variation of the elasticity coefficients among the variables without changing the cointegration relationship between the original time series, and the following results are obtained:Before model construction, unit root test and cointegration test need to be performed on the time series data. This paper adopts the Augmented Dickey–Fuller method to test the smoothness of the time series of carbon emission intensity and renewable energy consumption in China from 1997 to 2019 by sub-region. It is also necessary to conduct cointegration test on the time series data, and this paper adopts the AEG two-step method to test whether there is a cointegration relationship between carbon emission intensity and renewable energy consumption by sub-region. The specific steps are as follows: in the first step, ordinary least squares are used to do linear regression of a variable on other variables, and to obtain the residual series. In the second step, a smoothness test is conducted on the residual series obtained in the first step. If the residual series is not smooth, there is no cointegration relationship between multiple variables. If the residual series is smooth, there is a cointegration relationship between multiple variables.Impulse response function serves to study the dynamic impact process of other variables in the model in the current and later periods when the VAR model is hit by the standard deviation of one random error term, which can portray the dynamic interaction between variables and their effects in a more intuitive way. This method has also been widely used in this topic. For example, Soytas et al.  used genVariance decomposition is a method for studying the contribution of shocks to predict the variance of each variable to all endogenous variables in a VAR model. It can explain the extent to which changes in variables are caused by their own factors and the extent to which they are caused by shocks to other factors in the system. The variance decomposition describes information about the relative importance of each random disturbance that affects the variables in the VAR model. The impulse response function simply shows how each variable in the model responds to shocks over time, so if the importance of the influence relationship between variables is analyzed, then variance decomposition is required. That is, the variance decomposition can analyze how much the first variable itself and the other variables contribute to the change in the first variable in the future m periods respectively, in the given future m periods. The variance decomposition can be divided into the traditional orthogonal variance decomposition and the generalized prediction error variance decomposition, and the former is more commonly used at present. In this paper, the orthogonal variance decomposition is used to study the degree and mechanism of the mutual influence between renewable energy consumption and carbon emission intensity by sub-region.The descriptive statistical analysis of different regional variables is carried out, and the trend chart of the statistical results is shown in The trend diagram of economic growth rate and carbon emissions rate over time for the four major economic regions in China is shown in The decoupling results are shown in The unit root test for each variable shows that the first-order difference series of the original time series is smooth, so it can be concluded that the time series of LNRE and LNEIC of the four major economic regions is a first-order single integer. The results of the cointegration analysis are shown in Since no cointegration relationship exists for the northeastern time series data, it is not suitable for further modeling analysis. There is a co-integration relationship between renewable energy consumption and carbon emission intensity in the eastern, central and western regions. The VAR models are estimated by taking the third-order, second-order and second-order lags for the eastern, central and western regions, respectively.From the eastern region, the renewable energy consumption with the second- and third-order lags has a significantly negative effect on the carbon emission intensity, as shown in Further AR view tests were performed on the models of each of the three regions. The results show that the inverse of the mode of all characteristic roots is less than 1. They are all within the unit circle curve, which indicates that the model is stable and statistically sound, and could ensure the validity of impulse response analysis, variance decomposition and the reasonableness of data interpretation.In order to determine the causal relationship between renewable energy and carbon emission intensity in different regions, a Granger causality test is conducted separately. From the results of the eastern region, renewable energy consumption and carbon emission intensity show a two-way causal relationship. From the results of the central region, renewable energy consumption is the Granger cause of carbon emission intensity, but carbon emission intensity is not the Granger cause of renewable energy consumption. It shows that there is a one-way causal relationship. From the results of the western region, renewable energy consumption is the Granger cause of carbon emission intensity, but carbon emission intensity is not the Granger cause of renewable energy consumption. It indicates that there is a one-way causal relationship. The results of impulse response analysis are shown in For the central region, LNEIC has a strong response to the impact of its own one standard new spread immediately, with an impact rate of 0.081 in period 1, then gradually falling to a low of −0.005 in period 6 before returning to a positive value of 0.006 in period 7, and then leveling off, which shows that LNEIC adjusts more slowly to the impact of its own one standard new spread. The shock of LNEIC to LNRE’s one standard new spread does not respond in the current period, rises to a high of 0.024 in period 2, then drops to a negative value in period 3, reaches a low of −0.056 in period 6, and then stabilizes. The shock of LNRE to its own one standard new spread reacts strongly in the current period, reaching 0.156, then slowly fluctuates down and stabilizes after period 6. The shock of LNRE to one standard new spread of LNEIC reaches a minimum of −0.029 in the current period and then fluctuates and adjusts to −0.028 in period 5 before leveling off, which is not strong enough to explain itself.For the western region, LNEIC has a strong response to the shock of its own one standard new spread immediately, and starts to decline after reaching the highest point of 0.037 in period 2, reaching the lowest point of 0.015 in period 6, and then reflecting a smooth convergence to 0. LNEIC does not respond to the shock of LNRE one standard new spread in the current period, and then starts to decline, reflecting a negative shock effect, reaching the lowest point of −0.038 in period 5 after reaching a low of −0.038, after which it leveled off. LNRE’s shock to its own standard new spread immediately had a strong response, falling to 0.079 in period 2 and then rising to a high of 0.093 in period 5, then leveling off. LNRE’s shock to LNEIC’s standard new spread reached −0.01 in the current period, then rose to a positive effect, reaching a high of 0.017 and then leveled off, with the overall shock effect close to 0 and not obvious enough.The results of the variance decomposition are shown in From the variance decomposition of LNRE in the central region, the contribution rate of LNEIC to LNRE has been maintained at a low level, and the explanation effect is not obvious. From the variance decomposition of LNEIC in the central region, the contribution rate of LNRE to LNEIC has been maintained at a low level in the first three periods, and started to climb rapidly in the fourth period, with an obvious upward trend. This indicates that in the process of carbon emission intensity reduction, the increase of renewable energy consumption has a positive influence on the process of decreasing carbon emission intensity.From the variance decomposition of LNRE in the western region, the contribution rate of LNEIC to LNRE has been maintained at a low level, and the explanation effect is not obvious. From the variance decomposition of LNEIC in the western region, the contribution rate of LNRE to LNEIC has been maintained at a low level in the first two periods, and started to climb rapidly in the third period, with an obvious rising trend. It shows that in the process of decreasing carbon emission intensity, the increase of renewable energy consumption has a positive effect. As a whole, the energy market is closely related to carbon emissions, with the oil, coal and clean energy markets being closely linked. Thus, price fluctuations in any one of them may have spillover effects on the supply and demand of clean energy, and thus affect carbon emissions. Therefore, preventing potential risks in energy markets is also of great significance for carbon reduction.According to the decoupling analysis results, the decoupling index of carbon emissions and economic growth in China’s four major economic regions from 1998 to 2001 was the most ideal, which is consistent with the relevant research results of Zhang and Da (“”). The fAccording to the results of the co-integration test, the impulse response analysis and variance decomposition, there is a co-integration relationship between the renewable energy consumption and carbon emission intensity in the central, eastern and western regions. However, there is no long-term co-integration relationship between the two variables in the northeast region. This may be related to the low proportion of renewable energy consumption in the northeast, and its effect on reducing carbon emission intensity is not obvious. In the future, more attention should be paid to the energy structure of this region. The results of the impulse response analysis and variance decomposition show that renewable energy should be developed in the future, which will play a positive role in the optimization of energy structure in different regions and the reduction of carbon emission intensity.This paper draws the following conclusions through the empirical analysis and testing. Firstly, the decoupling status was generally better from 1998 to 2000, from the perspective of the four economic regions as a whole. This was due to the introduction of the “fifteen small enterprises” and “new five small enterprises” regulations during that period, which promoted the decoupling of carbon emissions. With the rapid economic growth, China’s overall decoupling status has changed, strong decoupling has also emerged in various economic regions with weak decoupling prevailing in recent years. Secondly, the decoupling status of the northeastern region is less than ideal and even negative decoupling has occurred in recent years, from the comparison of the four major economic regions. The eastern and western regions have a high proportion of renewable energy consumption, and the decoupling situation has been more ideal and stable over time. The economic growth of the central region is fast, but carbon emissions tend to grow faster than economic growth, the overall decoupling situation is not ideal. Thirdly, the impulse response analysis shows that the continuous development of renewable energy for the optimization of energy structure in different regions always maintain a long-term positive effect. Carbon emission intensity by renewable energy consumption would fluctuate in a short period of time, but maintain a stable and highly negative effect in the long term. Finally, based on the variance decomposition analysis, this paper concludes that the contribution of renewable energy consumption to carbon emission intensity in the three major economic regions has been increasing over time, with the fastest change in the western region. The explanation of carbon emission intensity to renewable energy also increases slowly in the eastern region, and it is not obvious enough in the central and western regions.First of all, China should actively respond to climate change, promote carbon emission reduction and achieve sustainable and healthy economic development according to the differences in factor endowments of different economic sub-regions. The eastern region could make use of its abundant capital to carry out a large amount of clean energy technology research and development, improve green transportation in smart cities and establish industrial energy utilization efficiency. The central region could rely on clean energy cooperation and development strategy of the Belt and Road to improve policy subsidies and tax rebates for renewable energy and natural gas and focus on implementing the policy of cutting excessive capacity. To achieve industrial upgrading of high energy-consuming enterprises, the central region may should realize green and low-carbon development. The western region is rich in clean energy resources, such as solar energy, wind energy and hydropower. Therefore, it needs to attract social investment with diversified financing such as PPP mode, and promote energy management with EMS mode to make the development and production of renewable energy in the west more intensive, professional and large-scale, while curbing production costs. Fossil energy consumption in the northeast is an important support for economic development. As an important hub for Sino-Russian natural gas cooperation projects, the northeast could take advantage of its unique location to gradually increase the proportion of natural gas consumption and effectively reduce carbon emissions.In addition, policymakers should improve China’s clean energy market risk monitoring and early warning mechanism to ensure stable supply of renewable energy. Affected by the COVID-19 epidemic and external extreme events such as the Russia-Ukraine war and natural disasters, the clean energy market is highly vulnerable to price fluctuations and jump risks. On the one hand, it is necessary to form effective price warnings, energy futures and establish a good national energy reserve to prevent major clean energy risks. On the other hand, social capital could be guided to increase capital investment in renewable energy technologies to make the supply of renewable energy more stable and secure, and to reduce power restrictions caused by technical reasons, natural disasters and so on.The regional carbon emission differences are considered comprehensively to coordinate effectively clean energy consumption. A unified national clean energy market should be established. In clean energy-rich regions, it is necessary to fully consider the impact of adverse factors on carbon emissions. For example, the eastern region should optimize energy management to reduce the systemic risk in the process of west–east power transmission. Then, the construction of a national carbon market should be strengthened. The regulation and initiative of the market should be improved by accelerating the legislative process, improving the regulatory mechanism, introducing more market players and trading varieties and further improving the carbon accounting system.Moreover, the leading role of the government in energy conservation and emission reduction should be played. Carbon peaks and carbon neutrality could not be achieved without the government’s efforts of industrial planning. The government should accelerate the transformation of green development, implement comprehensive conservation strategies, develop green and low-carbon industries, advocate green consumption and promote the formation of green and low-carbon modes of production and lifestyle. Carbon peaking and carbon neutrality have been actively and steadily promoted. Based on China’s advantages in energy resources, the government should further promote the energy revolution, strengthen the clean and efficient use of coal, accelerate the planning and construction of new energy systems, actively participate in global climate change governance and achieve the dual carbon goal. There are many papers in this field, and it is difficult to find new research perspectives theoretically. Data applied in this paper are secondary data. The situation in the four economic regions is bound to be limited by geographical location, resource richness and other factors. This paper only considers a macro perspective, without considering the micro complexity, and a follow-up study should pay more attention to the issues between different regions.There is much more to study in the area of carbon emissions, economic growth and renewable energy consumption, which can theoretically be studied at both micro and macro levels, e.g., the impact of a specific renewable energy consumption on the decoupling of carbon emissions and economic growth can be studied and the mechanism and transmission path of systemic risk among the three can also be deeply studied. Regarding methodology, an empirical model can be introduced across disciplines to further measure the degree of interaction between them.